A new independent component analysis for speech recognition and separation

IEEE Transactions on Audio, Speech and Language Processing

Volumn 14, Issue 4, 2006, Pages 1245-1254

  Chien, Jen Tzung ^a,b   Chen, Bo Cheng ^b  

^a IEEE   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Acoustic modeling; Blind source separation (BSS); Independent component analysis (ICA); Nonparametric likelihood ratio (NLR); Pronunciation variation; Speech recognition; Unsupervised learning

Indexed keywords

ACOUSTIC MODELING; KERNEL DENSITY FUNCTIONS; NONPARAMETRIC LIKELIHOOD RATIO (NLR); PRONUNCIATION VARIATION;

ALGORITHMS; BLIND SOURCE SEPARATION; MAXIMUM LIKELIHOOD; PROBABILITY DENSITY FUNCTION; SPEECH RECOGNITION; SPEECH SYNTHESIS; STATISTICAL METHODS; UNSUPERVISED LEARNING;

INDEPENDENT COMPONENT ANALYSIS;

EID: 34047275941     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSA.2005.858061     Document Type: Article

References (40)

1. T. W. Anderson, An Introduction to Multivariate Statistical Analysis. New York: Wiley, 1984.
2. H. Attias, "Independent factor analysis," Neural Comput., vol. 11, pp. 803-851, 1999.
3. L. R. Bahl, P. V. de Souza, P. S. Gopalakrishnan, D. Nahamoo, and M. A. Picheny, "Decision trees for phonological rules in continuous speech," in Proc. Int. Conf. Acoustic, Speech, Signal Processing (ICASSP), 1991, pp. 185-188.
4. A. J. Bell and T. J. Sejnowski, "An information-maximization approach to blind separation and blind deconvolution," Neural Comput., vol. 7, pp. 1129-1159, 1995.
5. Y. Blanco and S. Zazo, "New Gaussianity measures based on order statistics: application to ICA," Neurocomputing, vol. 51, pp. 303-320, 2003.
6. R. Boscolo, H. Pan, and V. P. Roychowdhury, "Independent component analysis based on nonparametric density estimation," IEEE Trans. Neural Netw., vol. 15, no. 1, pp. 55-65, Jan. 2004.
7. J.-F. Cardoso, "High-order contrasts for independent component analysis," Neural Comput., vol. 11, pp. 157-192, 1999.
8. J.-T. Chien, "Online hierarchical transformation of hidden Markov models for speech recognition," IEEE Trans. Speech Audio Process., vol. 7, no. 6, pp. 656-667, Nov. 1999.
9. _, "Decision tree state tying using cluster validity criteria," IEEE Trans. Speech Audio Process., vol. 13, no. 2, pp. 182-193, Mar. 2005.
10. J.-T. Chien and C.-H. Huang, "Bayesian learning of speech duration models," IEEE Trans. Speech Audio Process., vol. 11, no. 6, pp. 558-567, Nov. 2003.
11. P. Comon, "Independent component analysis, a new concept?," Signal Process., vol. 36, pp. 287-314, 1994.
12. J. Eriksson and V. Koivunen, "Characteristic-function-based independent component analysis," Signal Process., vol. 83, pp. 2195-2208, 2003.
13. J. Eriksson, J. Karvanen, and V. Koivunen, "Source distribution adaptive maximum likelihood estimation of ICA model," in Proc. Int. Workshop Independent Component Analysis and Blind Signal Separation, 2000, pp. 227-232.
14. T. Fukada, T. Yoshimura, and Y. Sagisaka, "Automatic generation of multiple pronunciations based on neural networks," Speech Commun., vol. 27, pp. 63-73, 1999.
15. N. C. Giri, Multirariate Statistical Analysis. New York: Marcel Dekker, 1996.
16. A. Hyvarinen, "Survey on independent component analysis," Neural Comput. Surveys, vol. 2, pp. 94-128, 1999.
17. A. Hyvarinen and E. Oja, "Independent component analysis: algorithms and applications," Neural Netw., vol. 13, pp. 411-430, 2000.
18. C. Huang, T. Chen, S. Li, E. Chang, and J. Zhou, "Analysis of speaker variability," in Proc. Eur. Conf. Speech Communication Technology (EUROSPEECH), 2001, pp. 1377-1380.
19. S. Ikeda, "Factor analysis preprocessing for ICA," in Proc. Int. Workshop Independent Component Analysis Blind Signal Separation, 2000, pp. 327-332.
20. G.-J. Jang, T.-W. Lee, and Y.-H. Oh, "Learning statistically efficient features for speaker recognition," Neurocomputers, no. 49, pp. 329-348, 2002.
21. A. Kaban and M. Girolami, "Clustering of text documents by skewness maximization," in Proc. Int. Workshop Independent Component Analysis Blind Signal Separation, 2000, pp. 435-440.
22. J. Karhunen and S. Malaroiu, "Locally linear independent component analysis," in Proc. Int. Joint Conf. Neural Networks, vol. 2, 1999, pp. 882-887.
23. T. Kosaka, S. Matsunaga, and S. Sagayama, "Speaker-independent speech recognition based on tree structured speaker clustering," Comput. Speech Language, vol. 10, pp. 55-74, 1996.
24. R. H. Lambert and A. J. Bell, "Blind separation of multiple speakers in a multipath environment," in Proc. Int. Conf. Acoustic, Speech, Signal Processing (ICASSP), vol. 1, 1997, pp. 423-426.
25. J.-H. Lee, H.-Y. Jung, T.-W. Lee, and S. Y. Lee, "Speech feature extraction using independent component analysis," in Proc. Int. Conf. Acoustic, Speech, Signal Processing (ICASSP), vol. 3, 2000, pp. 1631-1634.
26. T.-W. Lee, M. S. Lewicki, and T. J. Sejnowski, "ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1078-1089, Oct. 2000.
27. P. Meinicke and H. Ritter, "Independent component analysis with quantizing density estimators," in Proc. Int. Workshop Independent Component Analysis Blind Signal Separation, 2001, pp. 224-229.
28. N. Murata, "Properties of the empirical characteristic function and its application to testing for independence," in Proc. Int. Workshop Independent Component Analysis Blind Signal Separation, 2001, pp. 19-24.
29. E. Oja, 'The nonlinear PCA learning rule in independent component analysis," Neurocomputers, vol. 17, pp. 25-45, 1997.
30. E. Oja and K. Kiviluoto, "Data analysis, visualization, and hidden factor discovery by unsupervised learning," in Proc. Int. Joint Conf. Neural Networks, 1999, pp. 3871-3876.
31. M. Padmanabhan, L. R. Bahl, D. Nahamoo, and M. A. Picheny, "Speaker clustering and transformation for speaker adaptation in speech recognition systems," IEEE Trans. Speech Audio Process., vol. 6, no. 1, pp. 71-77, Jan. 1998.
32. P. Pajunen and M. Girolami, "Implementing decisions in binary decision trees using independent component analysis," in Proc. Int. Workshop Independent Component Analysis Blind Signal Separation, 2000, pp. 483-487.
33. D. T. Pham, "Blind separation of instantaneous mixture of sources via an independent component analysis," IEEE Trans. Signal Process., vol. 44, no. 11, pp. 2768-2779, Nov. 1996.
34. L. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.
35. D. Schobben, K. Torkkola, and P. Smaragdis, "Evaluation of blind signal Separation methods," in froc. Int. Workshop Independent Component Analysis Blind Signal Separation, 1999, pp. 261-266.
36. I. Shafran and M. Ostendorf, "Acoustic model clustering based on syllable structure," Comput. Speech Language, vol. 17, pp. 311-318, 2003.
37. R. Singh, B. Raj, and R. M. Stern, "Automatic generation of subword units for speech recognition systems," IEEE Trans. Speech Audio Process., vol. 10, no. 2, pp. 89-99, Feb. 2002.
38. N. Vlassis and Y. Motomura, "Efficient source adaptivity in independent component analysis," IEEE Trans. Neural Netw., vol. 12, no. 3, pp. 559-566, May 2001.
39. H. H. Yang and S. Amari, "Adaptive on-line learning algorithms for blind separation-maximum entropy and minimum mutual information," Neural Comput., vol. 9, pp. 1457-1482, 1997.
40. P. C. Yuen and J. H. Lai, "Face representation using independent component analysis," Pattern Recognit., vol. 35, pp. 1247-1257, 2002.