Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 7135-7139

  Arora, Raman ^a   Livescu, Karen ^a  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

articulatory measurements; canonical correlation analysis; domain independence; MOCHA TIMIT; multi view learning; speaker independence; TIMIT; XRMB

Indexed keywords

CANONICAL CORRELATION ANALYSIS; DOMAIN INDEPENDENCES; MOCHA-TIMIT; MULTI-VIEW LEARNING; SPEAKER-INDEPENDENCE; TIMIT; XRMB;

LINGUISTICS; SIGNAL PROCESSING; SPEECH RECOGNITION;

CLASSIFICATION (OF INFORMATION);

EID: 84890540774     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6639047     Document Type: Conference Paper

References (38)

1. R. Haeb-Umbach and H. Ney, "Linear discriminant analysis for improved large vocabulary continuous speech recognition," in ICASSP, 1992
2. N. Kumar and A. G. Andreou, "Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition," Speech Comm., vol. 26, no. 4, pp. 283-297, 1998
3. H. Hermansky et al., "Tandem connectionist feature extraction for conventional HMM systems," in ICASSP, 2000
4. S. King et al., "Speech production knowledge in automatic speech recognition," Journal of the Acoustical Society of America, vol. 121, no. 2, pp. 723-742, 2007
5. J. R. Westbury, X-ray microbeam speech production database user's handbook, Waisman Center on Mental Retardation &Human Development, U.Wisconsin, Madison, WI, version 1.0 edition, June 1994
6. A. Wrench, "A new resource for production modeling in speech technology," in Workshop on Innovations in Speech Processing, 2001
7. K. Richmond, P. Hoole, and S. King, "Announcing the electromagnetic articulography (day 1) subset of the mngu0 articulatory corpus," in Interspeech, 2011
8. J. Frankel and S. King, "ASR-articulatory speech recognition," in Eurospeech, 2001
9. A. Wrench and K. Richmond, "Continuous speech recognition using articulatory data," in ICSLP, 2000
10. T. Toda, A.W. Black, and K. Tokuda, "Acoustic-to-articulatory inversion mapping with Gaussian mixture model," in ICSLP, 2004
11. B. Uria et al., "Deep architectures for articulatory inversion," in Interspeech, 2012
12. K. Markov et al., "Integration of articulatory and spectrum features based on the hybrid HMM/BN modeling framework," Speech Communication, vol. 48, pp. 161-175, 2006
13. L. Deng et al., "Production models as a structural basis for automatic speech recognition," Speech Comm., vol. 33, pp. 93-111, 1997
14. M. Richardson, J. Bilmes, and C. Diorio, "Hidden-articulator Markov models for speech recognition," Speech Comm., vol. 41, no. 2-3, pp. 511-529, 2003
15. V. Mitra, H. Nam, C. Y. Espy-Wilson, E. Saltzman, and L. Goldstein, "Gesture-based dynamic Bayesian network for noise robust speech recognition," in ICASSP, 2011
16. K. Livescu et al., "Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU summer workshop," in ICASSP, 2007
17. S. Bharadwaj et al., "Multiview acoustic feature learning using articulatory measurements," in Intl. Workshop on Stat. Machine Learning for Speech Processing, 2012
18. R. Arora and K. Livescu, "Kernel CCA for multi-view learning of acoustic features using articulatory measurements," in Symp. on Machine Learning in Speech and Language Processing (MLSLP), 2012
19. K. Sridharan and S. Kakade, "An information theoretic framework for multi-view learning," in Conference on Learning Theory, 2008
20. H. Hotelling, "Relations between two sets of variates," Biometrika, vol. 28, no. 3/4, pp. 321-377, 1936
21. D. R. Hardoon et al., "Canonical correlation analysis: An overview with application to learning methods," Neural Computation, vol. 16, no. 12, pp. 2639-2664, 2004
22. K. Chaudhuri et al., "Multi-view clustering via canonical correlation analysis," in ICML, 2009
23. K. Livescu and M. Stoehr, "Multi-view learning of acoustic features for speaker recognition," in ASRU, 2009
24. K. Choukri and G. Chollet, "Adaptation of automatic speech recognizers to new speakers using canonical correlation analysis techniques," Speech Comm., vol. 1, pp. 95-107, 1986
25. F. Rudzicz, "Adaptive kernel canonical correlation analysis for estimation of task dynamics from acoustics," in ICASSP, 2010
26. T. Kato et al., "An analysis of articulatory-acoustic data based on articulatory strokes," in ICASSP, 2009
27. D. Foster, S. Kakade, and T. Zhang, "Multi-view dimensionality reduction via canonical correlation analysis," Tech. Rep. TTIC-TR-2008-4, TTI-Chicago, 2008
28. K. Fukumizu et al., "Statistical consistency of Kernel Canonical Correlation Analysis," Journal of Machine Learning Research, vol. 8, pp. 361-383, 2007
29. M. Brand, "Incremental singular value decomposition of uncertain data with missing values," in Eur. Conf. on Comp. Vision, 2002
30. J. S. Garofolo et al., "DARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM," NASA STI/Recon Technical Report N, 1993
31. A. K. Halberstadt, Heterogeneous Acoustic Measurements and Multiple Classifiers for Speech Recognition, Ph.D. thesis, MIT, 1998
32. A. Jansen, S. Thomas, and H. Hermansky, "Intrinsic spectral analysis for zero and high resource speech recognition," in Interspeech, 2012
33. D. R. Hardoon and J. Shawe-Taylor, "Sparse canonical correlation analysis," Machine Learning, vol. 83, pp. 331-353, 2011
34. G. Potamianos and C. Neti, "Audio-visual speech recognition in challenging environments," in Eurospeech, 2003
35. S.-C. Jou, T. Schultz, M. Walliczek, F. Kraft, and A. Waibel, "Towards continuous speech recognition using surface electromyography, " in Interspeech, 2006
36. S. Narayanan et al., "A multimodal real-time MRI articulatory corpus for speech research," in Interspeech, 2011
37. K. Livescu, B. Zhu, and J. Glass, "On the phonetic information in ultrasonic microphone signals," in ICASSP, 2009
38. S. Yu, B. De Moor, and Y. Moreau, "Learning with heterogenous data sets by weighted multiple kernel canonical correlation analysis," in Machine Learning for Signal Processing (MLSP), 2007.