Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences
暂无分享,去创建一个
[1] Max Welling,et al. Product of experts , 2007, Scholarpedia.
[2] M. Kroul. Automatic Speech Segmentation Based on HMM , 2007 .
[3] Mark J. F. Gales,et al. Product of Gaussians for speech recognition , 2006, Comput. Speech Lang..
[4] Mark J. F. Gales,et al. Temporally varying model parameters for large vocabulary continuous speech recognition , 2005, INTERSPEECH.
[5] David Talkin,et al. A Robust Algorithm for Pitch Tracking ( RAPT ) , 2005 .
[6] Christopher K. I. Williams. How to Pretend That Correlated Variables Are Independent by Using Difference Observations , 2005, Neural Computation.
[7] Mark J. F. Gales,et al. Basis superposition precision matrix modelling for large vocabulary continuous speech recognition , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[8] Paul W. Fieguth,et al. A multimodal variational approach to learning and inference in switching state space models [speech processing application] , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[9] Li Deng,et al. Target-directed mixture dynamic models for spontaneous speech recognition , 2004, IEEE Transactions on Speech and Audio Processing.
[10] Peder A. Olsen,et al. Modeling inverse covariance matrices by basis expansion , 2002, IEEE Transactions on Speech and Audio Processing.
[11] John Scott Bridle,et al. Towards better understanding of the model implied by the use of dynamic features in HMMs , 2004, INTERSPEECH.
[12] Vincent Vanhoucke,et al. Mixtures of inverse covariances: covariance modeling for gaussian mixtures with applications to automatic speech recognition , 2004 .
[13] Shigeru Katagiri,et al. A theoretical analysis of speech recognition based on feature trajectory models , 2004, INTERSPEECH.
[14] Mark J. F. Gales,et al. Switching linear dynamical systems for speech recognition , 2003 .
[15] Li Deng,et al. Tracking vocal tract resonances using an analytical nonlinear predictor and a target-guided temporal constraint , 2003, INTERSPEECH.
[16] E. McDermott,et al. Recognition method with parametric trajectory generated from mixture distribution HMMs , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[17] Li Deng,et al. Coarticulation modeling by embedding a target-directed hidden trajectory model into HMM - model and training , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[18] Jeff A. Bilmes,et al. Buried Markov models: a graphical-modeling approach to automatic speech recognition , 2003, Comput. Speech Lang..
[19] L. Deng,et al. COARTICULATION MODELING BY EMBEDDING A -MODEL AND TRAINING TARGET-DIRECTED HIDDEN TRAJECTORY MODEL INTO HMM , 2003 .
[20] Keiichi Tokuda,et al. Eigenvoices for HMM-based speech synthesis , 2002, INTERSPEECH.
[21] Kevin S. Van Horn,et al. Rethinking derived acoustic features in speech recognition , 2002, INTERSPEECH.
[22] Shigeru Katagiri,et al. A recognition method with parametric trajectory synthesized using direct relations between static and dynamic feature vector time series , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[23] Seiichi Nakagawa,et al. A Survey on Automatic Speech Recognition , 2002 .
[24] Keiichi Tokuda,et al. Adaptation of pitch and spectrum for HMM-based speech synthesis using MLLR , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[25] Alex Acero,et al. Spoken Language Processing: A Guide to Theory, Algorithm and System Development , 2001 .
[26] Christopher K. I. Williams,et al. Products of Gaussians , 2001, NIPS.
[27] L Deng,et al. Spontaneous speech recognition using a statistical coarticulatory model for the vocal-tract-resonance dynamics. , 2000, The Journal of the Acoustical Society of America.
[28] Roland Kuhn,et al. Rapid speaker adaptation in eigenvoice space , 2000, IEEE Trans. Speech Audio Process..
[29] Jeff A. Bilmes,et al. Factored sparse inverse covariance matrices , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[30] Keiichi Tokuda,et al. Speech parameter generation algorithms for HMM-based speech synthesis , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[31] Z. Ma,et al. Spontaneous speech recognition using statistical dynamic models for the vocal - tract - resonance dy , 2000 .
[32] Keiichi Tokuda,et al. Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis , 1999, EUROSPEECH.
[33] Alex Acero,et al. Formant analysis and synthesis using hidden Markov models , 1999, EUROSPEECH.
[34] Mark J. F. Gales,et al. Semi-tied covariance matrices for hidden Markov models , 1999, IEEE Trans. Speech Audio Process..
[35] Guo Qing,et al. An new method used in HMM for modeling frame correlation , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[36] Tetsunori Kobayashi,et al. Partly hidden Markov model and its application to speech recognition , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[37] Keiichi Tokuda,et al. Hidden Markov models based on multi-space probability distribution for pitch pattern modeling , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[38] John S. Bridle,et al. The HDM: a segmental hidden dynamic model of coarticulation , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[39] Li Deng,et al. Initial evaluation of hidden dynamic models on conversational speech , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[40] Qing Guo,et al. An new method used in HMM for modeling frame correlation , 1999, ICASSP.
[41] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[42] Martin J. Russell,et al. Probabilistic-trajectory segmental HMMs , 1999, Comput. Speech Lang..
[43] Robert E. Donovan,et al. The IBM trainable speech synthesis system , 1998, ICSLP.
[44] Li Deng,et al. A dynamic, feature-based approach to the interface between phonology and phonetics for speech modeling and recognition , 1998, Speech Commun..
[45] Mark J. F. Gales,et al. Maximum likelihood linear transformations for HMM-based speech recognition , 1998, Comput. Speech Lang..
[46] G. Zweig,et al. Speech recognition using dynamic Bayesian networks , 1998 .
[47] Koichi Shinoda,et al. Acoustic modeling based on the MDL principle for speech recognition , 1997, EUROSPEECH.
[48] Biing-Hwang Juang,et al. Minimum classification error rate methods for speech recognition , 1997, IEEE Trans. Speech Audio Process..
[49] Chong Kwan Un,et al. Frame-correlated hidden Markov model based on extended logarithmic pool , 1997, IEEE Trans. Speech Audio Process..
[50] Li Deng,et al. Speaker-independent phonetic classification using hidden Markov models with state-conditioned mixtures of trend functions , 1997 .
[51] K. Koishida,et al. Vector quantization of speech spectral parameters using statistics of dynamic features , 1997 .
[52] Julia Hirschberg,et al. Progress in speech synthesis , 1997 .
[53] Keiichi Tokuda,et al. Speaker interpolation in HMM-based speech synthesis system , 1997, EUROSPEECH.
[54] Herbert Gish,et al. Parametric trajectory models for speech recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.
[55] Mari Ostendorf,et al. From HMM's to segment models: a unified view of stochastic modeling for speech recognition , 1996, IEEE Trans. Speech Audio Process..
[56] Kuldip K. Paliwal,et al. Speech Coding and Synthesis , 1995 .
[57] Keiichi Tokuda,et al. An algorithm for speech parameter generation from continuous mixture HMMs with dynamic features , 1995, EUROSPEECH.
[58] Philip C. Woodland,et al. Automatic speech synthesiser parameter estimation using HMMs , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[59] K. Tokuda,et al. Speech parameter generation from HMM using dynamic features , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[60] Chin-Hui Lee,et al. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..
[61] Andrej Ljolje,et al. Automatic speech segmentation for concatenative inventory selection , 1994, SSW.
[62] Xiaodong Sun,et al. Speech recognition using hidden Markov models with polynomial regression functions as nonstationary states , 1994, IEEE Trans. Speech Audio Process..
[63] Mark J. F. Gales,et al. The theory of segmental hidden Markov models , 1993 .
[64] Martin Russell,et al. A segmental HMM for speech pattern modelling , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[65] Satoshi Takahashi,et al. Phoneme HMMs constrained by frame correlations , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[66] Kuldip K. Paliwal,et al. Use of temporal correlation between successive frames in a hidden Markov model based speech recognizer , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[67] Keiichi Tokuda,et al. An adaptive algorithm for mel-cepstral analysis of speech , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[68] Aaron E. Rosenberg,et al. Improved acoustic modeling for speaker independent large vocabulary continuous speech recognition , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[69] Chin-Hui Lee,et al. Improvements in connected digit recognition using higher order spectral and energy features , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[70] Mari Ostendorf,et al. A Dynamical System Approach to Continuous Speech Recognition , 1991, HLT.
[71] Shigeru Katagiri,et al. ATR Japanese speech database as a tool of speech recognition and synthesis , 1990, Speech Commun..
[72] Mari Ostendorf,et al. A stochastic segment model for phoneme-based continuous speech recognition , 1989, IEEE Trans. Acoust. Speech Signal Process..
[73] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[74] C. J. Wellekens,et al. Explicit correlation in hidden Markov model for speech recognition , 1987 .
[75] Peter F. Brown,et al. The acoustic-modeling problem in automatic speech recognition , 1987 .
[76] Lalit R. Bahl,et al. Maximum mutual information estimation of hidden Markov model parameters for speech recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.
[77] Sadaoki Furui,et al. Speaker-independent isolated word recognition using dynamic features of speech spectrum , 1986, IEEE Trans. Acoust. Speech Signal Process..
[78] B.-H. Juang,et al. Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains , 1985, AT&T Technical Journal.
[79] R. Gray,et al. Vector quantization , 1984, IEEE ASSP Magazine.
[80] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[81] Andrew J. Viterbi,et al. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.