Abu–Mostafa, Y. S. (1989), “The Vapnik–Chervonenkis dimension: information versus complexity in learning,” Neural Computation 1, 312ndash;317.CrossRefGoogle Scholar

Akaike, H. (1974), “A new look at the statistical model identification,” IEEE Transactions on Automatic Control 19(6), 716–723.CrossRefGoogle Scholar

Akaike, H. (1980), “Likelihood and the Bayes procedure,” in J. M., Bernardo, M. H., DeGroot, D. V., Lindley & A. F. M., Smith, eds, Bayesian Statistics, University Press, Valencia, Spain, pp. 143–166.Google Scholar

Akita, Y., & Kawahara, T. (2004), “ Language model adaptation based on PLSA of topics and speakers,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 1045–1048.

Aldous, D. (1985), “Exchangeability and related topics,” École d'Été de Probabilités de Saint–Flour XIII1983, pp. 1–198.CrossRef

Anastasakos, T., McDonough, J., Schwartz, R., & Makhoul, J. (1996), “A compact model for speaker–adaptive training,” Proceedings of International Conference on Spoken LanguageProcessing (ICSLP), pp. 1137–1140.CrossRef

Anguera Miro, X., Bozonnet, S., Evans, N., et al. (2012), “Speaker diarization: A review of recent research,” IEEE Transactions on Audio, Speech, and Language Processing 20(2), 356–370.CrossRefGoogle Scholar

Antoniak, C. E. (1974), “Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems,” Annals of Statistics 2(6), 1152–1174.CrossRefGoogle Scholar

Attias, H. (1999), “Inferring parameters and structure of latent variable models by variational Bayes,” Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), pp. 21–30.

Axelrod, S., Gopinath, R., & Olsen, P. (2002), “Modeling with a subspace constraint on inverse covariance matrices,” Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 2177–2180.

Bahl, L. R., Brown, P. F., de Souza, P. V., & Mercer, R. L. (1986), “Maximum mutual information estimation of hidden Markov model parameters for speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 49–52.CrossRef

Barber, D. (2012), Bayesian Reasoning and Machine Learning, Cambridge University Press.Google Scholar

Barker, J., Vincent, E., Ma, N., Christensen, H., & Green, P. (2013), “The PASCAL CHiME speech separation and recognition challenge,” Computer Speech and Language 27, 621–633.CrossRefGoogle Scholar

Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970), “A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains,” The Annals of Mathematical Statistics, pp. 164–171.CrossRef

Beal, M. J. (2003), Variational algorithms for approximate Bayesian inference, PhD thesis, University of London.Google Scholar

Beal, M. J., Ghahramani, Z., & Rasmussen, C. E. (2002), “The infinite hidden Markov model,” Advances in Neural Information Processing Systems 14, 577–584.Google Scholar

Bellegarda, J. (2004), “Statistical language model adaptation: review and perspectives,” Speech Communication 42(1), 93–108.CrossRefGoogle Scholar

Bellegarda, J. R. (2000), “Exploiting latent semantic information in statistical language modeling,” Proceedings of the IEEE 88(8), 1279–1296.CrossRefGoogle Scholar

Bellegarda, J. R. (2002), “Fast update of latent semantic spaces using a linear transform framework,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 769–772.

Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003), “A neural probabilistic language model,” Journal of Machine Learning Research 3, 1137–1155.Google Scholar

Berger, J. O. (1985), Statistical Decision Theory and Bayesian Analysis, Second Edition, Springer–Verlag.CrossRefGoogle Scholar

Bernardo, J. M., & Smith, A. F. M. (2009), Bayesian Theory, Wiley.Google Scholar

Berry, M. W., Dumais, S. T., & O'Brien, G. W. (1995), “Using linear algebra for intelligent information retrieval,” SIAM Review 37(4), 573–595.CrossRefGoogle Scholar

Bilmes, J. A. (1998), A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models, Technical Report TR–97–021, International Computer Science Institute.Google Scholar

Bilmes, J., & Zweig, G. (2002), “The graphical models toolkit: An open source software system for speech and time–series processing,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3916–3919.

Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer.Google Scholar

Blackwell, D., & MacQueen, J. B. (1973), “Ferguson distribution via Pólya urn schemes,” The Annals of Statistics 1, 353–355.CrossRefGoogle Scholar

Blei, D., Griffiths, T., & Jordan, M. (2010), “The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies,” Journal of the ACM 57(2), article 7.CrossRefGoogle Scholar

Blei, D., Griffiths, T., Jordan, M., & Tenenbaum, J. (2004), “Hierarchical topic models and the nested Chinese restaurant process,” Advances in Neural Information Processing Systems 16, 17–24.Google Scholar

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003), “Latent Dirichlet allocation,” Journal of Machine Learning Research 3, 993–1022.Google Scholar

Brants, T., Popat, A. C., Xu, P., Och, F. J., & Dean, J. (2007), “Large language models in machine translation,” Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP–CoNLL), Association for Computational Linguistics, pp. 858–867.

Brill, E., & Moore, R. C. (2000), “An improved error model for noisy channel spelling correction,” Proceedings of the 38th Annual Meeting of Association for Computational Linguistics,Association for Computational Linguistics, pp. 286–293.CrossRef

Brown, P., Desouza, P., Mercer, R., Pietra, V., & Lai, J. (1992), “Class–based n–gram models of natural language,” Computational Linguistics 18(4), 467–479.Google Scholar

Brown, P. F., Cocke, J., Pietra, S. A. D., et al. (1990), “A statistical approach to machine translation,” Computational Linguistics 16(2), 79–85.Google Scholar

Campbell, W. M., Sturim, D. E., & Reynolds, D. A. (2006), “Support vector machines using GMM supervectors for speaker verification,” Signal Processing Letters, IEEE 13(5), 308–311.CrossRefGoogle Scholar

Chen, K.–T., Liau, W.–W., Wang, H.–M., & Lee, L.–S. (2000), “Fast speaker adaptation using eigenspace–based maximum likelihood linear regression,” Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 742–745.

Chen, S. F. (2009), “Shrinking exponential language models,” in Proceedings of Human Language Technologies : The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 468–476.CrossRef

Chen, S. F., & Goodman, J. (1999), “An empirical study of smoothing techniques for language modeling,” Computer Speech & Language 13(4), 359–393.CrossRefGoogle Scholar

Chen, S., & Gopinath, R. (1999), “Model selection in acoustic modeling,” Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH), pp. 1087– 1090.

Chesta, C., Siohan, O., & Lee, C.–H. (1999), “Maximum a posteriori linear regression for hidden Markov model adaptation,” Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH), pp. 211–214.

Chien, J.–T. (1999), “Online hierarchical transformation of hidden Markov models for speech recognition,” IEEE Transactions on Speech and Audio Processing 7(6), 656–667.Google Scholar

Chien, J.–T. (2002), “Quasi–Bayes linear regression for sequential learning of hidden Markov models,” IEEE Transactions on Speech and Audio Processing 10(5), 268–278.Google Scholar

Chien, J.–T. (2003), “Linear regression based Bayesian predictive classification for speech recognition,” IEEE Transactions on Speech and Audio Processing 11(1), 70–79.Google Scholar

Chien, J.–T., & Chueh, C.–H. (2011), “Dirichlet class language models for speech recognition,” IEEE Transactions on Audio, Speech, and Language Processing 19(3), 482–495.CrossRefGoogle Scholar

Chien, J.–T., Huang, C.–H., Shinoda, K., & Furu, S. (2006), “Towards optimal Bayes decision for speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 45–48.

Chien, J.–T., Lee, C.–H., & Wang, H.–C. (1997), “Improved Bayesian learning of hidden Markov models for speaker adaptation,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1027–1030.

Chien, J. T., & Liao, G.–H. (2001), “Transformation–based Bayesian predictive classification using online prior evolution,” IEEE Transactions on Speech and Audio Processing 9(4), 399–410.Google Scholar

Chien, J.–T., & Wu, M.–S. (2008), “Adaptive Bayesian latent semantic analysis,” IEEE Transactions on Audio, Speech, and Language Processing 16(1), 198–207.CrossRefGoogle Scholar

Chou, W., & Reichl, W., (1999), “Decision tree state tying based on penalized Bayesian information criterion,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 345–348.CrossRef

Coccaro, N., & Jurafsky, D. (1998), “Towards better integration of semantic predictors in statistical language modeling,” Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 2403–2406.

Cournapeau, D., Watanabe, S., Nakamura, A., & Kawahara, T. (2010), “Online unsupervised classification with model comparison in the variational Bayes framework for voice activity detection,” IEEE Journal of Selected Topics in Signal Processing 4(6), 1071–1083.CrossRefGoogle Scholar

Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012), “Context–dependent pre–trained deep neural networks for large–vocabulary speech recognition,” IEEE Transactions on Audio, Speech and Language Processing 20(1), 30–42.CrossRefGoogle Scholar

Davis, S. B., & Mermelstein, P. (1980), “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,” IEEE Transactions on Acoustics, Speech, and Signal Processing 28(4), 357–366.CrossRefGoogle Scholar

Dawid, A. P. (1981), “Some matrix–variate distribution theory: notational considerations and a Bayesian application,” Biometrika 68(1), 265–274.CrossRefGoogle Scholar

De Bruijn, N. G. (1970), Asymptotic Methods in Analysis, Dover Publications.Google Scholar

Dehak, N., Kenny, P., Dehak, R., Dumouchel, P., & Ouellet, P. (2011), “Front–end factor analysis for speaker verification,” IEEE Transactions on Audio, Speech, and Language Processing 19(4), 788–798.CrossRefGoogle Scholar

Delcroix, M., Nakatani, T., & Watanabe, S. (2009), “Static and dynamic variance compensation for recognition of reverberant speech with dereverberation preprocessing,” IEEE Transactions on Audio, Speech, and Language Processing 17(2), 324–334.CrossRefGoogle Scholar

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1976), “Maximum likelihood from incomplete data via the EM algorithm,” Journal of Royal Statistical Society B 39, 1–38.Google Scholar

Digalakis, V., & Neumeyer, L. (1996), “Speaker adaptation using combined transformation and Bayesian methods,” IEEE Transactions on Speech and Audio Processing 4, 294–300.CrossRefGoogle Scholar

Digalakis, V., Ritischev, D., & Neumeyer, L. (1995), “Speaker adaptation using constrained reestimation of Gaussian mixtures,” IEEE Transactions on Speech and Audio Processing 3, 357–366.CrossRefGoogle Scholar

Ding, N. & Ou, Z. (2010), “Variational nonparametric Bayesian hidden Markov model,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2098–2101.CrossRef

Droppo, J., Acero, A., & Deng, L. (2002), “Uncertainty decoding with SPLICE for noise robustn speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, pp. I–57.Google Scholar

Federico, M. (1996), “Bayesian estimation methods of n–gram language model adaptation,” Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 240–243.CrossRef

Ferguson, T. (1973), “A Bayesian analysis of some nonparametric problems,” The Annals of Statistics 1, 209–230.CrossRefGoogle Scholar

Fosler, E., & Morris, J. (2008), “Crandem systems: Conditional random field acoustic models for hidden Markov models,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4049–4052.

Fox, E. B., Sudderth, E. B., Jordan, M. I., & Willsky, A. S. (2008), “An HDP–HMM for systems with state persistence,” Proceedings of International Conference on Machine Learning (ICML), pp. 312–319.CrossRef

Fukunaga, K. (1990), Introduction to Statistical Pattern Recognition, Academic Press.Google Scholar

Furui, S. (1981), “Cepstral analysis technique for automatic speaker verification,” IEEE Transactions on Acoustics, Speech and Signal Processing 29(2), 254–272.CrossRefGoogle Scholar

Furui, S. (1986), “Speaker independent isolated word recognition using dynamic features of speech spectrum,” IEEE Transactions on Acoustics, Speech and Signal Processing 34, 52–59.CrossRefGoogle Scholar

Furui, S. (2010), “History and development of speech recognition,” in Speech Technology, F, Chen and K, Jokinen, eds., Springer, pp. 1–18.Google Scholar

Furui, S., Maekawa, K., & H. Isahara, M. (2000), “A Japanese national project on spontaneous speech corpus and processing technology,” Proceedings of ASR'00, pp. 244–248.

Gales, M. (1998), “Maximum likelihood linear transformations for HMM–based speech recognition,” Computer Speech and Language 12, 75–98.CrossRefGoogle Scholar

Gales, M., Center, I., & Heights, Y. (2000), “Cluster adaptive training of hidden Markov models,” IEEE Transactions on Speech and Audio Processing 8(4), 417–428.CrossRefGoogle Scholar

Gales, M. J. F. (1999), “Semi–tied covariance matrices for hidden Markov models,” IEEE Transactions on Speech and Audio Processing 7(3), 272–281.CrossRefGoogle Scholar

Gales, M. J. F., & Woodland, P. C. (1996), Variance compensation within the MLLR framework, Technical Report 242, Cambridge University Engineering Department.Google Scholar

Gales, M., Watanabe, S., & Fossler–Lussier, E. (2012), “Structured discriminative models for speech recognition,” IEEE Signal Processing Magazine 29(6), 70–81.CrossRefGoogle Scholar

Ganapathiraju, A., Hamaker, J., & Picone, J. (2004), “Applications of support vector machines to speech recognition,” IEEE Transactions on Signal Processing 52(8), 2348–2355.CrossRefGoogle Scholar

Gaussier, E., & Goutte, C. (2005), “Relation between PLSA and NMF and implications,” Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 601–602.CrossRef

Gauvain, J.–L., & Lee, C.–H. (1991), “Bayesian learning of Gaussian mixture densities for hidden Markov models,” Proceedings of DARPA Speech and Natural Language Workshop, pp. 272–277.CrossRef

Gauvain, J.–L., & Lee, C.–H. (1994), “Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains,” IEEE Transactions on Speech and Audio Processing 2, 291–298.CrossRefGoogle Scholar

Gelman, A., Carlin, J. B., Stern, H. S., et al. (2013), Bayesian Data Analysis, CRC Press.Google Scholar

Geman, S., & Geman, D. (1984), “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence 6(1), 721–741.Google ScholarPubMed

Genkin, A., Lewis, D. D., & Madigan, D. (2007), “Large–scale Bayesian logistic regression for text categorization,” Technometrics 49(3), 291–304.CrossRefGoogle Scholar

Ghahramani, Z. (1998), “Learning dynamic Bayesian networks,” in Adaptive Processing of Sequences and Data Structures, Springer, pp. 168–197.Google Scholar

Ghahramani, Z. (2004), “Unsupervised learning,” Advanced Lectures on Machine Learning, pp. 72–112.CrossRef

Ghosh, J. K., Delampady, M., & Samanta, T. (2007), An Introduction to Bayesian Analysis: Theory and Methods, Springer.Google Scholar

Gildea, D., & Hofmann, T. (1999), “Topic–based language models using EM,” Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH), pp. 2167–2170.

Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1996), Markov Chain Monte Carlo in Practice, Chapman & Hall/CRC Interdisciplinary Statistics.

Gish, H., Siu, M.–h., Chan, A., & Belfield, W. (2009), “Unsupervised training of an HMM–based speech recognizer for topic classification,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 1935–1938.

Glass, J. (2003), “A probabilistic framework for segment–based speech recognition,” Computer Speech & Language 17(2–3), 137–152.CrossRefGoogle Scholar

Goel, V., & Byrne, W. (2000), “Minimum Bayes–risk automatic speech recognition,” Computer Speech and Language 14, 115–135.CrossRefGoogle Scholar

Goldwater, S. (2007), Nonparametric Bayesian models of lexical acquisition, PhD thesis, Brown University.Google Scholar

Goldwater, S., & Griffiths, T. (2007), “A fully Bayesian approach to unsupervised part–of–speech tagging,” Proceedings of Annual Meeting of the Association of Computational Linguistics, pp. 744–751.

Goldwater, S., Griffiths, T., & Johnson, M. (2009), “A Bayesian framework for word segmentation: Exploring the effects of context,” Cognition 112(1), 21–54.CrossRefGoogle Scholar

Goldwater, S., Griffiths, T. L., & Johnson, M. (2006), “Interpolating between types and tokens by estimating power–law generators,” Advances in Neural Information Processing Systems 18.Google Scholar

Good, I. J. (1953), “The population frequencies of species and the estimation of populations,” Biometrika 40, 237–264.CrossRefGoogle Scholar

Grézl, F., Karafiát, M., Kontár, S., & Cernocky, J. (2007), “Probabilistic and bottle–neck features for LVCSR of meetings,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 757–760.CrossRef

Griffiths, T., & Ghahramani, Z. (2005), Infinite latent feature models and the Indian buffet process, Technical Report, Gatsby Unit.

Griffiths, T., & Steyvers, M. (2004), “Finding scientific topics,” in Proceedings of the National Academy of Sciences, 101 Suppl. 1, 5228–5235.CrossRefGoogle ScholarPubMed

Gunawardana, A., Mahajan, M., Acero, A., & Platt, J. C. (2005), “Hidden conditional random fields for phone classification,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 1117–1120.

Haeb–Umbach, R., & Ney, H. (1992), “Linear discriminant analysis for improved large vocabulary continuous speech recognition,” International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, pp. 13–16.Google Scholar

Hahm, S. J., Ogawa, A., Fujimoto, M., Hori, T., & Nakamura, A. (2012), “Speaker adaptation using variational Bayesian linear regression in normalized feature space,” in Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 803–806.

Hashimoto, K., Nankaku, Y., & Tokuda, K. (2009), “A Bayesian approach to hidden semi–Markov model based speech synthesis,” in Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 1751–1754.

Hashimoto, K., Zen, H., Nankaku, Y., Lee, A., & Tokuda, K. (2008), “Bayesian context clustering using cross valid prior distribution for HMM–based speech recognition,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 936–939.

Hashimoto, K., Zen, H., Nankaku, Y., Masuko, T., & Tokuda, K. (2009), “A Bayesian approach to HMM–based speech synthesis,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2009, pp. 4029–4032.CrossRef

Hastings, W. K. (1970), “Monte Carlo sampling methods using Markov chains and their applications,” Biometrika 57, 97–109.CrossRefGoogle Scholar

Heigold, G., Ney, H., Schluter, R., & Wiesler, S. (2012), “Discriminative training for automatic speech recognition: Modeling, criteria, optimization, implementation, and performance,” IEEE Signal Processing Magazine 29(6), 58–69.CrossRefGoogle Scholar

Hermansky, H. (1990), “Perceptual linear predictive (PLP) analysis of speech,” Journal of the Acoustic Society of America 87(4), 1738–1752.CrossRefGoogle Scholar

Hermansky, H., Ellis, D., & Sharma, S. (2000), “Tandem connectionist feature extraction for conventional HMM systems,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1635–1638.CrossRef

Hinton, G., Deng, L., Yu, D., et al. (2012), “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Processing Magazine 29(6), 82–97.CrossRefGoogle Scholar

Hinton, G., Osindero, S., & Teh, Y. (2006), “A fast learning algorithm for deep belief nets,” Neural Computation 18, 1527–1554.CrossRefGoogle ScholarPubMed

Hofmann, T. (1999a), “Probabilistic latent semantic analysis,” Proceedings of Uncertainty in Artificial Intelligence, pp. 289–296.

Hofmann, T. (1999b), “Probabilistic latent semantic indexing,” Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57.

Hofmann, T. (2001), “Unsupervised learning by probabilistic latent semantic analysis,” Machine Learning 42(1–2), 177–196.CrossRefGoogle Scholar

Hori, T., & Nakamura, A. (2013), “Speech recognition algorithms using weighted finite–state transducers,” Synthesis Lectures on Speech and Audio Processing 9(1), 1–162.Google Scholar

Hu, R., & Zhao, Y. (2007), “Knowledge–based adaptive decision tree state tying for conversational speech recognition,” IEEE Transactions on Audio, Speech, and Language Processing 15(7), 2160–2168.CrossRefGoogle Scholar

Huang, S., & Renals, S. (2008), “Unsupervised language model adaptation based on topic and role information in multiparty meeting,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 833–836.

Huang, X. D., Acero, A., & Hon, H.W. (2001), Spoken Language Processing: A Guide to Theory, Algorithm, and System Development, Prentice Hall.Google Scholar

Huang, X. D., Ariki, Y., & Jack, M. A. (1990), Hidden Markov Models for Speech Recognition, Edinburgh University Press.Google Scholar

Huo, Q., & Lee, C.–H. (1997), “On–line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate,” IEEE Transactions on Speech and Audio Processing 5(2), 161–172.Google Scholar

Huo, Q, & Lee, C.–H. (2000), “A Bayesian predictive classification approach to robust speech recognition,” IEEE Transactions on Speech and Audio Processing 8, 200–204.Google Scholar

Ishiguro, K., Yamada, T., Araki, S., Nakatani, T., & Sawada, H. (2012), “Probabilistic speaker diarization with bag–of–words representations of speaker angle information,” IEEE Transactions on Audio, Speech, and Language Processing 20(2), 447–460.CrossRefGoogle Scholar

Jansen, A., Dupoux, E., Goldwater, S., et al. (2013), “A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 8111–8115.CrossRef

Jelinek, F. (1976), “Continuous speech recognition by statistical methods,” Proceedings of the IEEE 64(4), 532–556.CrossRefGoogle Scholar

Jelinek, F. (1997), Statistical Methods for Speech Recognition, MIT Press.Google Scholar

Jelinek, F., & Mercer, R. L. (1980), “Interpolated estimation of Markov source parameters from sparse data,” Proceedings of the Workshop on Pattern Recognition in Practice, pp. 381–397.

Ji, S., Xue, Y., & Carin, L. (2008), “Bayesian compressive sensing,” IEEE Transactions on Signal Processing 56(6), 2346–2356.CrossRefGoogle Scholar

Jiang, H., Hirose, K., & Huo, Q. (1999), “Robust speech recognition based on a Bayesian prediction approach,” IEEE Transactions on Speech and Audio Processing 7, 426–440.Google Scholar

Jitsuhiro, T., & Nakamura, S. (2004), “Automatic generation of non–uniform HMM structures based on variational Bayesian approach,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 805–808.CrossRef

Joachims, T. (2002), “Learning to classify text using support vector machines: Methods, theory, and algorithms,” Computational Linguistics 29(4), 656–664.Google Scholar

Jordan, M., Ghahramani, Z., Jaakkola, T., & Saul, L. (1999), “An introduction to variational methods for graphical models,” Machine Learning 37(2), 183–233.CrossRefGoogle Scholar

Juang, B.–H., & Rabiner, L. (1990), “The segmental K–means algorithm for estimating parameters of hidden Markov models,” IEEE Transactions on Acoustics, Speech and Signal Processing 38(9), 1639–1641.CrossRefGoogle Scholar

Juang, B., & Katagiri, S. (1992), “Discriminative learning for minimum error classification,” IEEE Transactions on Signal Processing 40(12), 3043–3054.CrossRefGoogle Scholar

Jurafsky, D. (2014), “From languages to information,” http://www.stanford.edu/class/cs124/lec/ languagemodeling.pdf.

Jurafsky, D., & Martin, J. H. (2000), Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Prentice Hall.Google Scholar

Kass, R. E., & Raftery, A. E. (1993), Bayes factors and model uncertainty, Technical Report 254, Department of Statistics, University of Washington.Google Scholar

Kass, R. E., & Raftery, A. E. (1995), “Bayes factors,” Journal of the American Statistical Association 90(430), 773–795.CrossRefGoogle Scholar

Katz, S. (1987), “Estimation of probabilities from sparse data for the language model component of a speech recognizer,” IEEE Transactions on Acoustics, Speech, and Signal Processing 35(3), 400–401.CrossRefGoogle Scholar

Kawabata, T., & Tamoto, M. (1996), “Back–off method for n–gram smoothing based on binomial posteriori distribution,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, pp. 192–195.Google Scholar

Kenny, P. (2010), “Bayesian speaker verification with heavy tailed priors,” Keynote Speech, Odyssey Speaker and Language Recognition Workshop.

Kenny, P., Boulianne, G., Ouellet, P., & Dumouchel, P. (2007), “Joint factor analysis versus eigenchannels in speaker recognition,” IEEE Transactions on Audio, Speech, and Language Processing 15(4), 1435–1447.CrossRefGoogle Scholar

Kingsbury, B., Sainath, T. N., & Soltau, H. (2012), “Scalable minimum Bayes risk training of deep neural network acoustic models using distributed Hessian–free optimization,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 10–13.

Kinnunen, T., & Li, H. (2010), “An overview of text–independent speaker recognition: from features to supervectors,” Speech Communication 52(1), 12–40.CrossRefGoogle Scholar

Kita, K. (1999), Probabilistic Language Models, University of Tokyo Press (in Japanese).Google Scholar

Kneser, R., & Ney, H. (1995), “Improved backing–off for m–gram language modeling,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 181–184.CrossRef

Kneser, R., Peters, J., & Klakow, D. (1997), “Language model adaptation using dynamic marginals,” Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH), pp. 1971–1974.

Kolossa, D., & Haeb–Umbach, R. (2011), Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications, Springer.CrossRefGoogle Scholar

Kubo, Y., Watanabe, S., Nakamura, A., & Kobayashi, T. (2010), “A regularized discriminative training method of acoustic models derived by minimum relative entropy discrimination,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2954–2957.

Kudo, T. (2005), “Mecab: Yet another part–of–speech and morphological analyzer,” http://mecab.sourceforge. net/.

Kuhn, R., & De Mori, R. (1990), “A cache–based natural language model for speech recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence 12(6), 570–583.CrossRefGoogle Scholar

Kuhn, R., Junqua, J., Ngyuen, P., & Niedzielski, N. (2000), “Rapid speaker adaptation in eigenvoice space,” IEEE Transactions on Speech and Audio Processing 8(6), 695–707.CrossRefGoogle Scholar

Kullback, S., & Leibler, R. A. (1951), “On information and sufficiency,” Annals of Mathematical Statistics 22(1), 79–86.CrossRefGoogle Scholar

Kwok, J. T.–Y. (2000), “The evidence framework applied to support vector machines,” IEEE Transactions on Neural Networks 11(5), 1162–1173.Google ScholarPubMed

Kwon, O., Lee, T.–W., & Chan, K. (2002), “Application of variational Bayesian PCA for speech feature extractio,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, pp. 825–828.Google Scholar

Lafferty, J., McCallum, A., & Pereira, F. (2001), “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” Proceedings of International Conference on Machine Learning, pp. 282–289.

Lamel, L., Gauvain, J.–L., & Adda, G. (2002), “Lightly supervised and unsupervised acoustic model training,” Computer Speech & Language 16(1), 115–129.CrossRefGoogle Scholar

Lau, R., Rosenfeld, R., & Roukos, S. (1993), “Trigger–based language models: A maximum entropy approach,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 2, IEEE, pp. 45–48.Google Scholar

Lee, C.–H., & Huo, Q. (2000), “On adaptive decision rules and decision parameter adaptation for automatic speech recognition,” Proceedings of the IEEE 88, 1241–1269.Google Scholar

Lee, C.–H., Lin, C.–H., & Juang, B.–H. (1991), “A study on speaker adaptation of the parameters of continuous density hidden Markov models,” IEEE Transactions on Acoustics, Speech, and Signal Processing 39, 806–814.Google Scholar

Lee, C.–Y. (2014), Discovering linguistic structures in speech: models and applications, PhD thesis, Massachusetts Institute of Technology.

Lee, C.–Y.,&Glass., J. (2012), “A nonparametric Bayesian approach to acoustic model discovery,” Proceedings of Annual Meeting of the Association for Computational Linguistics, pp. 40–49.

Lee, C.–Y., Zhang, Y., & Glass, J. (2013), “Joint learning of phonetic units and word pronunciations for ASR,” Proceedings of the 2013 Conference on Empirical Methods on Natural Language Processing (EMNLP), pp. 182–192.

Lee, D. D., & Seung, H. S. (1999), “Learning the parts of objects by non–negative matrix factorization,” Nature 401(6755), 788–791.Google ScholarPubMed

Leggetter, C. J., & Woodland, P. C. (1995), “Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models,” Computer Speech and Language 9, 171–185.CrossRefGoogle Scholar

Lewis, D. D. (1998), “Naive (Bayes) at forty: The independence assumption in information retrieval,” Proceedings of the 10th European Conference on Machine Learning, Springer–Verlag, pp. 4–15.Google Scholar

Liu, J. (1994), “The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem,” Journal of the American Statistical Association 89(427).CrossRefGoogle Scholar

Liu, J. S. (2008), Monte Carlo Strategies in Scientific Computing, Springer.Google Scholar

Livescu, K., Glass, J. R., & Bilmes, J. (2003), “Hidden feature models for speech recognition using dynamic Bayesian networks,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2529–2532.

MacKay, D. J. C. (1992a), “Bayesian interpolation,” Neural Computation 4(3), 415–447.CrossRefGoogle Scholar

MacKay, D. J. C. (1992b), “The evidence framework applied to classification networks,” Neural Computation 4(5), 720–736.CrossRefGoogle Scholar

MacKay, D. J. C. (1992c), “A practical Bayesian framework for back–propagation networks,” Neural Computation 4(3), 448–472.CrossRefGoogle Scholar

MacKay, D. J. C. (1995), “Probable networks and plausible predictions – a review of practical Bayesian methods for supervised neural networks,” Network: Computation in Neural Systems 6(3), 469–505.CrossRefGoogle Scholar

MacKay, D. J. C. (1997), Ensemble learning for hidden Markov models, Technical Report, Cavendish Laboratory, University of Cambridge.Google Scholar

MacKay, D. J. C., & Peto, L. C. B. (1995), “A hierarchical Dirichlet language model,” Natural Language Engineering 1(3), 289–308.CrossRefGoogle Scholar

Maekawa, T., & Watanabe, S. (2011<), “Unsupervised activity recognition with user's physical characteristics data,” Proceedings of International Symposium on Wearable Computers, pp. 89–96.

Mak, B., Kwok, J., & Ho, S. (2005), “Kernel eigenvoice speaker adaptation,” IEEE Transactions on Speech and Audio Processing 13(5), 984–992.CrossRefGoogle Scholar

Manning, C. D., & Schütze, H. (1999), Foundations of Statistical Natural Language Processing, MIT Press.Google Scholar

Masataki, H., Sagisaka, Y., Hisaki, K., & Kawahara, T. (1997), “Task adaptation using MAP estimation in n–gram language modeling,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 783–786.CrossRef

Matsui, T., & Furui, S. (1994), “Comparison of text–independent speaker recognition methods using VQ–distortion and discrete/continuous HMMs,” IEEE Transactions on Speech and Audio Processing 2(3), 4567ndas459;459.CrossRefGoogle Scholar

Matsumoto, Y., Kitauchi, A., Yamashita, T., et al. (1999), “Japanese morphological analysis system ChaSen version 2.0 manual,” NAIST Technical Report.

McCallum, A., & Nigam, K. (1998), “A comparison of event models for naive Bayes text classification,” in Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) Workshop on Learning for Text Categorization, Vol. 752, pp. 41–48.Google Scholar

McDermott, E., Hazen, T., Le Roux, J., Nakamura, A., & Katagiri, S. (2007), “Discriminative training for large–vocabulary speech recognition using minimum classification error,” IEEE Transactions on Audio, Speech, and Language Processing 15(1), 203–223.CrossRefGoogle Scholar

Meignier, S., Moraru, D., Fredouille, C., Bonastre, J.–F., & Besacier, L. (2006), “Step–by–step and integrated approaches in broadcast news speaker diarization,” Computer Speech & Language 20(2), 303–330.CrossRefGoogle Scholar

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953), “Equation of state calculations by fast computing machines,” Journal of Chemical Physics 21(6), 1087–1092.CrossRefGoogle Scholar

Minka, T. P. (2001), “Expectation propagation for approximate Bayesian inference,” Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI), pp. 362–369.

Mochihashi, D., Yamada, T., & Ueda, N. (2009), “Bayesian unsupervised word segmentation with nested Pitman–Yor language modeling,” Proceedings of Joint Conference of Annual Meeting of the ACL and International Joint Conference on Natural Language Processing of the AFNLP, pp. 100–108.CrossRef

Mohri, M., Pereira, F., & Riley, M. (2002), “Weighted finite–state transducers in speech recognition,” Computer Speech and Language 16, 69–88.CrossRefGoogle Scholar

Moraru, D., Meignier, S., Besacier, L., Bonastre, J.–F., & Magrin–Chagnolleau, I. (2003), “The ELISA consortium approaches in speaker segmentation during the NIST 2002 speaker recognition evaluation,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 2, pp. 89–92.Google Scholar

Mrva, D., & Woodland, P. C. (2004), “A PLSA–based language model for conversational telephone speech,” in Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2257–2260.

Murphy, K. P. (2002), Dynamic Bayesian networks: representation, inference and learning, PhD thesis, University of California, Berkeley.Google Scholar

Murphy, K. P., Weiss, Y., & Jordan, M. I. (1999), “Loopy belief propagation for approximate inference: An empirical study,” Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI), pp. 467–475.

Nadas, A. (1985), “Optimal solution of a training problem in speech recognition,” IEEE Transactions on Acoustics, Speech and Signal Processing 33(1), 326–329.CrossRefGoogle Scholar

Nakagawa, S. (1988), Speech Recognition by Probabilistic Model, Institute of Electronics, Information and Communication Engineers (IEICE) (in Japanese).

Nakamura, A., McDermott, E., Watanabe, S., & Katagiri, S. (2009), “A unified view for discriminative objective functions based on negative exponential of difference measure between strings,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1633–1636.CrossRef

Neal, R., & Hinton, G. (1998), “A view of the EM algorithm that justifies incremental, sparse, and other variants,” Learning in Graphical Models, pp. 355–368.CrossRef

Neal, R. M. (1992), “Bayesian mixture modeling,” Proceedings of the Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis 11, 197–211.Google Scholar

Neal, R. M. (1993), “Probabilistic inference using Markov chain Monte Carlo methods,” Technical Report CRG–TR–93–1, Dept. of Computer Science, University of Toronto.Google Scholar

Neal, R. M. (2000), “Markov chain sampling methods for Dirichlet process mixture models,” Journal of Computational and Graphical Statistics 9(2), 249–265.Google Scholar

Neal, R. M. (2003), “Slice sampling,” Annals of Statistics 31, 705–767.

Nefian, A. V., Liang, L., Pi, X., Liu, X., & Murphy, K. (2002), “Dynamic Bayesian networks for audio–visual speech recognition,” EURASIP Journal on Applied Signal Processing 11, 1274–1288.Google Scholar

Neubig, G.,Mimura, M.,Mori, S., & Kawahara, T. (2010), “Learning a language model from continuous speech,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 1053–1056.

Ney, H., Essen, U., & Kneser, R. (1994), “On structuring probabilistic dependences in stochastic language modeling,” Computer Speech and Language 8, 1–38.CrossRefGoogle Scholar

Ney, H., Haeb–Umbach, R., Tran, B.–H., & Oerder, M. (1992), “Improvements in beam search for 10000–word continuous speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, IEEE, pp. 9–12.Google Scholar

Niesler, T., & Willett, D. (2002), “Unsupervised language model adaptation for lecture speech transcription,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 1413–1416.

Normandin, Y. (1992), “Hidden Markov models, maximum mutual information estimation, and the speech recognition problem,” PhD thesis, McGill University, Montreal, Canada.Google Scholar

Odell, J. J. (1995), The use of context in large vocabulary speech recognition, PhD thesis, Cambridge University.Google Scholar

Ostendorf, M., & Singer, H. (1997), “HMM topology design using maximum likelihood successive state splitting,” Computer Speech and Language 11, 17–41.CrossRefGoogle Scholar

Paul, D. B., & Baker, J. M. (1992), “The design for the Wall Street Journal–based CSR corpus,” Proceedings of theWorkshop on Speech and Natural Language, Association for Computational Linguistics, pp. 357–362.CrossRef

Pettersen, S. (2008), Robust speech recognition in the presence of additive noise, PhD thesis,Norwegian University of Science and Technology.Google Scholar

Pitman, J. (2002), “Poisson–Dirichlet and GEM invariant distributions for split–and–merge transformation of an interval partition,” Combinatorics, Probability and Computing 11, 501–514.CrossRefGoogle Scholar

Pitman, J. (2006), Combinatorial Stochastic Processes, Springer–Verlag.Google Scholar

Pitman, J., & Yor, M. (1997), “The two–parameter Poisson–Dirichlet distribution derived from a stable subordinator,” Annals of Probability 25(2), 855–900.Google Scholar

Porteous, I., Newman, D., Ihler, A., et al. (2008), “Fast collapsed Gibbs sampling for latent Dirichlet allocation,” Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 569–577.CrossRef

Povey, D. (2003), Discriminative training for large vocabulary speech recognition, PhD thesis, Cambridge University.Google Scholar

Povey, D., Burget, L., Agarwal, M., et al. (2010), “Subspace Gaussian mixture models for speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4330–4333.CrossRef

Povey, D., Gales, M. J. F., Kim, D., & Woodland, P. C. (2003), “MMI–MAP and MPE–MAP for acoustic model adaptation,” Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH) 8, 1981–1984.Google Scholar

Povey, D., Ghoshal, A., Boulianne, G., et al. (2011), “The Kaldi speech recognition toolkit,” Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).

Povey, D., Kanevsky, D., Kingsbury, B., et al. (2008), “Boosted MMI for model and feature–space discriminative training,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4057–4060.CrossRef

Povey, D., Kingsbury, B., Mangu, L., et al. (2005), “fMPE: Discriminatively trained features for speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 1, 961–964.Google Scholar

Povey, D., & Woodland, P. C. (2002), “Minimum phone error and I–smoothing for improved discriminative training,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 13–17.Google Scholar

Povey, D., Woodland, P., & Gales, M. (2003), “Discriminative MAP for acoustic model adaptation,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 1, I–312.CrossRefGoogle Scholar

Price, P., Fisher, W., Bernstein, J., & Pallett, D. (1988), “The DARPA 1000–word resource management database for continuous speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 651–654.CrossRef

Rabiner, L. R., & Juang, B.–H. (1986), “An introduction to hidden Markov models,” IEEE ASSP Magazine 3(1), 4–16.CrossRefGoogle Scholar

Rabiner, L. R., & Juang, B.–H. (1993), Fundamentals of Speech Recognition, Vol. 14, PTR Prentice Hall.Google Scholar

Rasmussen, C. E. (1999), “The infinite Gaussian mixture model,” Advances in Neural Information Processing Systems 12, 554–560.Google Scholar

Rasmussen, C. E., & Williams, C. K. I. (2006), Gaussian Processes for Machine Learning, Adaptive Computation and Machine Learning, MIT Press.Google Scholar

Reynolds, D., Quatieri, T., & Dunn, R. (2000), “Speaker verification using adapted Gaussian mixture models,” Digital Signal Processing 10(1–3), 19–41.CrossRefGoogle Scholar

Rissanen, J. (1984), “Universal coding, information, prediction and estimation,” IEEE Transactions on Information Theory 30, 629–636.CrossRefGoogle Scholar

Rissanen, J. (1984), “Universal coding, information, prediction and estimation,” IEEE Transactions on Information Theory 30, 629–636.CrossRefGoogle Scholar

Rodriguez, A., Dunson, D. B., & Gelfand, A. E. (2008), “The nested Dirichlet process,” Journal of the American Statistical Association 103(483), 1131–1154.CrossRefGoogle Scholar

Rosenfeld, R. (2000), “Two decades of statistical language modeling: Where do we go from here?,” Proceedings of the IEEE 88(8), 1270–1278.CrossRefGoogle Scholar

Sainath, T. N., Ramabhadran, B., Picheny, M., Nahamoo, D., & Kanevsky, D. (2011), “Exemplarbased sparse representation features: from TIMIT to LVCSR,” IEEE Transactions on Audio, Speech and Language Processing 19(8), 2598–2613.CrossRefGoogle Scholar

Saito, D., Watanabe, S., Nakamura, A., & Minematsu, N. (2012), “Statistical voice conversion based on noisy channel model,” IEEE Transactions on Audio, Speech, and Language Processing 20(6), 1784–1794.CrossRefGoogle Scholar

Salakhutdinov, R. (2009), Learning deep generative models, PhD thesis, University of Toronto.Google Scholar

Salton, G., & Buckley, C. (1988), “Term–weighting approaches in automatic text retrieval,” Information Processing & Management 24(5), 513–523.CrossRefGoogle Scholar

Sanderson, C., Bengio, S., & Gao, Y. (2006), “On transforming statistical models for non–frontal face verification,” Pattern Recognition 39(2), 288–302.CrossRefGoogle Scholar

Sankar, A., & Lee, C.–H. (1996), “A maximum–likelihood approach to stochastic matching for robust speech recognition,” IEEE Transactions on Speech and Audio Processing 4(3), 190–202.CrossRefGoogle Scholar

Saon, G., & Chien, J.–T. (2011), “Some properties of Bayesian sensing hidden Markov models,” Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 65–70.CrossRef

Saon, G., & Chien, J.–T. (2012a), “Bayesian sensing hidden Markov models,” IEEE Transactions on Audio, Speech, and Language Processing 20(1), 43–54.CrossRefGoogle Scholar

Saon, G., & Chien, J.–T. (2012b), “Large–vocabulary continuous speech recognition systems: A look at some recent advances,” IEEE Signal Processing Magazine 29(6), 18–33.CrossRefGoogle Scholar

Schalkwyk, J., Beeferman, D., Beaufays, F., et al. (2010), “ ‘Your word is my command’: Google search by voice: A case study,” in Advances in Speech Recognition, Springer, pp. 61–90.Google Scholar

Schlüter, R., Macherey, W., Müller, B., & Ney, H. (2001), “Comparison of discriminative training criteria and optimization methods for speech recognition,” Speech Communication 34(3), 287–310.CrossRefGoogle Scholar

Schultz, T., & Waibel, A. (2001), “Language–independent and language–adaptive acoustic modeling for speech recognition,” Speech Communication 35(1), 31–51.CrossRefGoogle Scholar

Schwarz, G. (1978), “Estimating the dimension of a model,” The Annals of Statistics 6(2), 461–464.CrossRefGoogle Scholar

Scott, S. (2002), “Bayesian methods for hidden Markov models,” Journal of the American Statistical Association 97(457), 337–351.CrossRefGoogle Scholar

Seide, F., Li, G., Chen, X., & Yu, D. (2011), “Conversational speech transcription using context dependent deep neural networks,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 437–440.

Sethuraman, J. (1994), “A constructive definition of Dirichlet priors,” Statistica Sinica 4, 639–650.Google Scholar

Shikano, K., Kawahara, T., Kobayashi, T., et al. (1999), Japanese Dictation Toolkit – Free Software Repository for Automatic Speech Recognition, http://www.ar.media.kyotou.ac.jp/dictation/.

Shinoda, K. (2010), “Acoustic model adaptation for speech recognition,” IEICE Transactions on Information and Systems 93(9), 2348–2362.Google Scholar

Shinoda, K., & Inoue, N. (2013), “Reusing speech techniques for video semantic indexing,” IEEE Signal Processing Magazine 30(2), 118–122.CrossRefGoogle Scholar

Shinoda, K., & Iso, K. (2001), “Efficient reduction of Gaussian components using MDL criterion for HMM–based speech recognition,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 869–872.Google Scholar

Shinoda, K., & Lee, C.–H. (2001), “A structural Bayes approach to speaker adaptation,” IEEE Transactions on Speech and Audio Processing 9, 276–287.CrossRefGoogle Scholar

Shinoda, K., & Watanabe, T. (1996), “Speaker adaptation with autonomous model complexity control by MDL principle,” Proceedings of International Conference on Acoustic, Speech, and Signal Processing (ICASSP), pp. 717–720.CrossRef

Shinoda, K., & Watanabe, T. (1997), “Acoustic modeling based on the MDL criterion for speech recognition,” Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH), Vol. 1, pp. 99–102.

Shinoda, K., & Watanabe, T. (2000), “MDL–based context–dependent subword modeling for speech recognition,” Journal of the Acoustical Society of Japan (E) 21, 79–86.CrossRefGoogle Scholar

Shiota, S., Hashimoto, K., Nankaku, Y., & Tokuda, K. (2009), “Deterministic annealing based training algorithm for Bayesian speech recognition,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 680–683.

Siohan, O., Myrvoll, T. A., & Lee, C. H. (2002), “Structural maximum a posteriori linear regression for fast HMM adaptation,” Computer Speech and Language 16(1), 5–24.CrossRefGoogle Scholar

Siu, M.–h., Gish, H., Chan, A., Belfield, W., & Lowe, S. (2014), “Unsupervised training of an HMM–based self–organizing unit recognizer with applications to topic classification and keyword discovery,” Computer Speech & Language 28(1), 210–223.CrossRefGoogle Scholar

Somervuo, P. (2004), “Comparison of ML, MAP, and VB based acoustic models in large vocabulary speech recognition,” Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 830–833.

Spiegelhalter, D. J., & Lauritzen, S. L. (1990), “Sequential updating of conditional probabilities on directed graphical structures,” Networks 20(5), 579–605.CrossRefGoogle Scholar

Sproat, R., Gale, W., Shih, C., & Chang, N. (1996), “A stochastic finite–state word–segmentation algorithm for Chinese,” Computational Linguistics 22(3), 377–404.Google Scholar

Stenger, B., Ramesh, V., Paragios, N., Coetzee, F., & Buhmann, J. M. (2001), “Topology free hidden Markov models: Application to background modeling,” Proceedings of International Conference on Computer Vision (ICCV)', Vol. 1, pp. 294–301.Google Scholar

Stolcke, A., Ferrer, L., Kajarekar, S., Shriberg, E., & Venkataraman, A. (2005), “MLLR transforms as features in speaker recognition,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2425–2428.

Stolcke, A., & Omohundro, S. (1993), “Hidden Markov model induction by Bayesian model merging,” Advances in Neural Information Processing Systems, pp. 11–18, Morgan Kaufmann.Google Scholar

Takahashi, J., & Sagayama, S. (1997), “Vector–field–smoothed Bayesian learning for fast and incremental speaker/telephone–channel adaptation,” Computer Speech and Language 11, 127–146.CrossRefGoogle Scholar

Takami, J., & Sagayama, S. (1992), “A successive state splitting algorithm for efficient allophone modeling,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 573–576.CrossRef

Tam, Y.–C., & Schultz, T. (2005), “Dynamic language model adaptation using variational Bayes inference,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 5–8.

Tam, Y.–C., & Schultz, T. (2006), “Unsupervised language model adaptation using latent semantic marginals,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2206–2209.

Tamura, M., Masuko, T., Tokuda, K., & Kobayashi, T. (2001), “Adaptation of pitch and spectrum for HMM–based speech synthesis using MLLR,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 805–808.CrossRef

Tawara, N., Ogawa, T., Watanabe, S., & Kobayashi, T. (2012a), “Fully Bayesian inference of multi–mixture Gaussian model and its evaluation using speaker clustering,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5253–5256.

Tawara, N., Ogawa, T., Watanabe, S., Nakamura, A., & Kobayashi, T. (2012b), “Fully Bayesian speaker clustering based on hierarchically structured utterance–oriented Dirichlet process mixture model,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2166–2169.

Teh, Y. W. (2006), “A hierarchical Bayesian language model based on Pitman–Yor processes,” Proceedings of International Conference on Computational Linguistics and Annual Meeting of the Association for Computational Linguistics, pp. 985–992.CrossRef

Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006), “Hierarchical Dirichlet processes,” Journal of the American Statistical Association 101(476), 1566–1581.CrossRefGoogle Scholar

Tipping, M. E. (2001), “Sparse Bayesian learning and the relevance vector machine,” Journal of Machine Learning Research 1, 211–244.Google Scholar

Torbati, A. H. H. N., Picone, J., & Sobel, M. (2013), “Speech acoustic unit segmentation using hierarchical Dirichlet processes,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 637–641.

Ueda, N., & Ghahramani, Z. (2002), “Bayesian model search for mixture models based on optimizing variational bounds,” Neural Networks 15, 1223–1241.CrossRefGoogle ScholarPubMed

Valente, F. (2006), “Infinite models for speaker clustering,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 1329–1332.

Valente, F., Motlicek, P., & Vijayasenan, D. (2010), “Variational Bayesian speaker diarization of meeting recordings,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4954–4957.CrossRef

Valente, F., & Wellekens, C. (2003), “Variational Bayesian GMM for speech recognition,” Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH), pp. 441–444.

Valente, F.,& Wellekens, C. (2004a), “Variational Bayesian feature selection for Gaussian mixture models,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, pp. 513–516.Google Scholar

Valente, F., & Wellekens, C. (2004b), “Variational Bayesian speaker clustering,” Proceedings of ODYSSEY The Speaker and Language Recognition Workshop, pp. 207–214.

Vapnik, V. (1995), The Nature of Statistical Learning Theory, Springer–Verlag.CrossRefGoogle Scholar

Veselỳ, K., Ghoshal, A., Burget, L., & Povey, D. (2013), “Sequence–discriminative training of deep neural networks,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2345–2349.

Villalba, J., & Brümmer, N. (2011), “Towards fully Bayesian speaker recognition: Integrating out the between–speaker covariance,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 505–508.

Vincent, E., Barker, J., Watanabe, S., et al. (2013), “The second ‘CHiME’ speech separation and recognition challenge: An overview of challenge systems and outcomes,” Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 162–167.CrossRef

Viterbi, A. J. (1967), “Error bounds for convolutional codes and an asymptotically optimal decoding algorithm,” IEEE Transactions on Information Theory IT–13, 260–269.Google Scholar

Wallach, H. M. (2006), “Topic modeling: beyond bag–of–words,” Proceedings of International Conference on Machine Learning, pp. 977–984.CrossRef

Watanabe, S., & Chien, J. T. (2012), “Tutorial: Bayesian learning for speech and language processing,” International Conference on Acoustics, Speech, and Signal Processing (ICASSP).

Watanabe, S., Minami, Y., Nakamura, A., & Ueda, N. (2002), “Application of variational Bayesian approach to speech recognition,” Advances in Neural Information Processing Systems.

Watanabe, S., Minami, Y., Nakamura, A., & Ueda, N., (2004), “Variational Bayesian estimation and clustering for speech recognition,” IEEE Transactions on Speech and Audio Processing 12, 365–381.CrossRefGoogle Scholar

Watanabe, S., & Nakamura, A. (2004), “Acoustic model adaptation based on coarse–fine training of transfer vectors and its application to a speaker adaptation task,” Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 2933–2936.

Watanabe, S., & Nakamura, A. (2006), “Speech recognition based on Student's t–distribution derived from total Bayesian framework,” IEICE Transactions on Information and Systems E89–D, 970–980.Google Scholar

Watanabe, S., & Nakamura, A. (2009), “On–line adaptation and Bayesian detection of environmental changes based on a macroscopic time evolution system,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4373–4376.CrossRef

Watanabe, S., Nakamura, A., & Juang, B. (2011), “Bayesian linear regression for hidden Markov model based on optimizing variational bounds,” Proceedings of IEEE Workshop on Machine Learning for Signal Processing, pp. 1–6.CrossRef

Watanabe, S., Nakamura, A., & Juang, B.–H. (2013), “Structural Bayesian linear regression for hidden Markov models,” Journal of Signal Processing Systems, 1–18.Google Scholar

Wegmann, S., McAllaster, D., Orloff, J., & Peskin, B. (1996), “Speaker normalization on conversational telephone speech,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 339–341.CrossRef

Winn, J., & Bishop, C. (2006), “Variational message passing,” Journal of Machine Learning Research 6(1), 661.Google Scholar

Witten, I. H., & Bell, T. C. (1991), “The zero–frequency problem: estimating the probabilities of novel events in adaptive text compression,” IEEE Transactions on Information Theory 37, 1085–1094.CrossRefGoogle Scholar

Wooters, C., Fung, J., Peskin, B., & Anguera, X. (2004), “Towards robust speaker segmentation: The ICSI–SRI fall 2004 diarization system,” in RT–04F Workshop, Vol. 23.

Wooters, C., & Huijbregts, M. (2008), “The ICSI RT07s speaker diarization system,” in Multimodal Technologies for Perception of Humans, Springer, pp. 509–519.Google Scholar

Yamagishi, J., Kobayashi, T., Nakano, Y., Ogata, K., & Isogai, J. (2009), “Analysis of speaker adaptation algorithms for HMM–based speech synthesis and a constrained SMAPLR adaptation algorithm,” IEEE Transactions on Audio, Speech, and Language Processing 17(1), 66–83.CrossRefGoogle Scholar

Yaman, S., Chien, J.–T., & Lee, C.–H. (2007), “Structural Bayesian language modeling and adaptation,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2365–2368.

Yedidia, J. S., Freeman, W. T., & Weiss, Y. (2003), “Understanding belief propagation and its generalizations,” Exploring Artificial Intelligence in the New Millennium 8, 236–239.Google Scholar

Young, S., Evermann, G., Gales, M., et al. (2006), “The HTK book (for HTK version 3.4),” Cambridge University Engineering Department.Google Scholar

Young, S. J., Odell, J. J., & Woodland, P. C. (1994), “Tree–based state tying for high accuracy acoustic modelling,” Proceedings of the Workshop on Human Language Technology, pp. 307–312.CrossRef

Yu, K., & Gales, M. J. F. (2006), “Incremental adaptation using Bayesian inference,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 217–220.CrossRef

Zhang, Y., & Glass, J. R. (2009), “Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams,” Proceedings of IEEE Automatic Speech Recognition & Understanding Workshop (ASRU), pp. 398–403.CrossRef

Zhang, Y., Liu, P., Chien, J.–T., & Soong, F. (2009), “An evidence framework for Bayesian learning of continuous–density hidden Markov models,” Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3857–3860.CrossRef

Zhao, X., Dong, Y., Zhao, J., et al. (2009), “Variational Bayesian joint factor analysis for speaker verification,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4049–4052.

Zhou, B., & Hansen, J. H. (2000), “Unsupervised audio stream segmentation and clustering via the Bayesian information criterion,” Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 714–717.

Zweig, G., & Nguyen, P. (2009), “A segmental CRF approach to large vocabulary continuous speech recognition,” Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 152–157.CrossRef

Zweig, G., & Russell, S. (1998), “Speech recognition with dynamic Bayesian networks,” Proceedings of the National Conference Artificial Intelligence, pp. 173–180.