Automatic speech recognition using articulatory features from subject-independent acoustic-to-articulatory inversion

Journal of the Acoustical Society of America

Volumn 130, Issue 4, 2011, Pages

  Ghosh, Prasanta Kumar ^a   Narayanan, Shrikanth ^a  

^a University of Southern California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICULATORY FEATURES; ARTICULATORY INVERSION; AUTOMATIC SPEECH RECOGNITION; CLASSIFICATION ACCURACY; PHONETIC CLASSIFICATION; SPEAKING STYLES; SPEECH ACOUSTICS;

FEATURE EXTRACTION;

SPEECH RECOGNITION;

ARTICLE; AUTOMATIC SPEECH RECOGNITION; AUTOMATION; FEMALE; HISTOLOGY; HUMAN; JAW; MALE; MOUTH; PHONATION; PHONETICS; PHYSIOLOGY; SPEECH; SPEECH ANALYSIS; STATISTICAL MODEL; THEORETICAL MODEL;

AUTOMATION; FEMALE; HUMANS; JAW; LINEAR MODELS; MALE; MODELS, THEORETICAL; MOUTH; PHONATION; PHONETICS; SPEECH ACOUSTICS; SPEECH PRODUCTION MEASUREMENT; SPEECH RECOGNITION SOFTWARE;

EID: 82255163922     PISSN: 00014966     EISSN: None     Source Type: Journal    
DOI: 10.1121/1.3634122     Document Type: Article

References (19)

1. J. Frankel and S. King, ASR-articulatory speech recognition., in Proceedings of Eurospeech, Scandinavia, (2001), pp. 599-602.
2. L. Deng, G. Ramsay, and D. Sun, Production models as a structural basis for automatic speech recognition., Speech Commun. 22 (2), 93-112 (1997).
3. H. Attias, L. Lee, and L. Deng, Variational inference and learning for segmental switching state space models of hidden speech dynamics., Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, China, Vol. 1 (2003), pp. I-872-I-875.
4. J. Ma and L. Deng, Target-directed mixture dynamic models for spontaneous speech recognition., IEEE Trans. Speech Audio Process. 12 (1), 47-58 (2004).
5. E. McDermott and A. Nakamura, Production-oriented models for speech recognition., IEICE Trans. Inf. Syst. E89-D (3), 1006-1014 (2006).
6. F. Metze and A. Waibel, A flexible stream architecture for ASR using articulatory features., International Conference on Spoken Language Processing, Denver, CO, USA (2002), pp. 2133-2136.
7. J. Hogden, A. Lofqvist, V. Gracco, I. Zlokarnik, P. Rubin, and E. Saltzman, Accurate recovery of articulator positions from acoustics: New conclusions based on human data., J. Acoust. Soc. Am. 100 (3), 1819-1834 (1996). (Pubitemid 26307570)
8. H. Yehia, A study on the speech acoustic-to-articulatory mapping using morphological constraints., Ph.D. thesis, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan, 2002.
9. T. Toda, A. Black, K. Tokuda, Statistical mapping between articulatory movements and acoustic spectrum using a gaussian mixture model., Speech Commun. 50, 215-217 (2008).
10. P. K. Ghosh and S. S. Narayanan, A subject-independent acoustic-to-articulatory inversion., Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Prague, Czech Republic (2011), pp. 4624-4627.
11. D. Yu, L. Deng, and A. Acero, Speaker-adaptive learning of resonance targets in a hidden trajectory model of speech coarticulation., Comput. Speech Lang. 27, 72-87 (2007). (Pubitemid 44537646)
12. A. A. Wrench and H. J. William, A multichannel articulatory database and its application for automatic speech recognition., 5th Seminar on Speech Production: Models and Data, Bavaria, (2000), pp. 305-308.
13. J. Silva, V. Rangarajan, V. Rozgic, and S. S. Narayanan, Information theoretic analysis of direct articulatory measurements for phonetic discrimination., Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Honolulu, HI, USA, (2007), pp. 457-460.
14. C. P. Browman and L. Goldstein, Towards an articulatory phonology., Phonol. Yearbook 3, 219-252 (1986).
15. John S. Garofolo, TIMIT Acoustic-Phonetic Continuous Speech Corpus., Linguistic Data Consortium, Philadelphia, PA (1993).
16. P. K. Ghosh and S. S. Narayanan, A generalized smoothness criterion for acoustic-to- articulatory inversion., J. Acoust. Soc. Am. 128 (4), 2162-2172 (2010).
17. B. Pellom and K. Hacioglu, Sonic: The University of Colorado continuous speech recognizer., Technical Report No. TR-CSLR-2001-01 (2005).
18. A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm., J. R. Stat. Soc. Ser. B (Methodol.) 39 (1), 1-38 (1977).
19. M. Hollander and D. A. Wolfe, Nonparametric Statistical Methods (Wiley, New Jersey, 1999).