Towards speaker adaptive training of deep neural network acoustic models

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2014, Pages 2189-2193

  Miao, Yajie ^a   Zhang, Hao ^a   Metze, Florian ^a  

^a Carnegie Mellon University   (United States)

Author keywords

Automatic speech recognition; Deep neural networks; Speaker adaptive training

Indexed keywords

SPEECH COMMUNICATION; VECTOR SPACES;

ACOUSTIC MODEL; AUTOMATIC SPEECH RECOGNITION; DEEP NEURAL NETWORKS; FEATURE MAPPING; FEATURE SPACE; SPEAKER ADAPTATION; SPEAKER ADAPTIVE TRAININGS; WORD ERROR RATE REDUCTIONS;

SPEECH RECOGNITION;

EID: 84910031119     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. G. Dahl, D. Yu, L. Deng, and A. Acero, "Context dependent pre-trained deep neural networks for large vocabulary speech recognition, " IEEE Transactions on Audio, Speech and Language Processing, vol. 20(1), pp. 30-42, 2012.
2. F. Seide, G. Li, X. Chen, and D. Yu, "Feature engineering in context-dependent deep neural networks for conversational speech transcription, " in Proc. ASRU, pp. 24-29, 2011.
3. M. Gales, "Maximum likelihood linear transformations for HMM-based speech recognition, " Computer Speech and Language, vol. 12, pp. 75-98, 1998.
4. B. Li, and K. C. Sim, "Comparison of discriminative input and output transformations for speaker adaptation in the hybrid NN/HMM systems, " in Proc. Inter speech, pp. 526-529, 2010.
5. K. Yao, D. Yu, F. Seide, H. Su, L. Deng, and Y. Gong, "Adaptation of context-dependent deep neural networks for automatic speech recognition, " in Proc. IEEE Spoken Language Technology Workshop, pp. 366-369, 2012.
6. S. M. Siniscalchi, J. Li, and C.-H. Lee, "Hermitian-based hidden activation functions for adaptation of hybrid HMM/ANN models, " in Proc. Inter speech, pp. 526-529, 2012.
7. S. P. Rath, D. Povey, K. Vesely, and J. Cernocky, "Improved feature processing for deep neural networks, " in Proc. Inter speech, 2013.
8. G. Saon, H. Soltau, D. Nahamoo, and M. Picheny, "Speaker adaptation of neural network acoustic models using i-vectors, " in Proc. ASRU, pp. 55-59, 2013.
9. N. Dehak, R. Dehak, P. Kenny, N. Brummer, P. Ouellet, and P. Dumouchel, "Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification, " in Proc. Inter speech, pp. 1559- 1562, 2009.
10. N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, "Front-end factor analysis for speaker verification, " IEEE Transactions on Audio, Speech and Language Processing, vol. 19, no. 4, pp. 788-798, 2011.
11. O. Glembek, L. Burget, P. Matejka, M. Karafiat, and P. Kenny, "Simplification and optimization of i-vector extraction, " in Proc. ICASSP, pp. 4516-4519, 2011.
12. T. Anastasakos, J. McDonough, and J. Makhoul, "Speaker adaptive training: A maximum likelihood approach to speaker normalization, " in Proc. ICASSP, pp. 1043-1046, 1997.
13. O. Abdel-Hamid, and H. Jiang, "Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code, " in Proc. ICASSP, pp. 7942-7946, 2013.
14. O. Abdel-Hamid, and H. Jiang, "Rapid and effective speaker adaptation of convolutional neural network based models for speech recognition, " in Proc. Inter speech, 2013.
15. P. Kenny, P. Ouellet, N. Dehak, V. Gupta, and P. Dumouchel, "A study of interspeaker variability in speaker verification, " IEEE Transactions on Audio, Speech and Language Processing, vol. 16, no. 5, pp. 980- 988, 2008.
16. M. Karafiat, L. Burget, P. Matejka, O. Glembek, and J. Cernocky, "iVector-based discriminative adaptation for automatic speech recognition, " in Proc. ASRU, pp. 152- 157, 2011.
17. D. Povey, B. Kingsbury, L. Mangu, G. Saon, H. Soltau, and G. Zweig, "fMPE: discriminatively trained features for speech recognition, " in Proc. ICASSP, pp. 961-964, 2005.
18. Y. Miao, F. Metze, and A. Waibel, "Learning discriminative basis coefficients for eigen space MLLR unsupervised adaptation, " in Proc. ICASSP, pp. 7927- 7931, 2013.
19. B. Zhang, S. Matsoukas, and R. Schwartz, "Recent progress on the discriminative region-dependent transform for speech feature extraction, " in Proc. Inter speech, 2006.
20. O. Abdel-Hamid, A. Mohamed, H. Jiang, and G. Penn, "Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition, " in Proc. ICASSP, pp. 4277-4280, 2012.
21. T. N. Sainath, A. Mohamed, B. Kingsbury, and B. Ramabhadran, "Deep convolutional neural networks for LVCSR, " in Proc. ICASSP, pp. 8614-8618, 2013.
22. D. Povey, A. Ghoshal, et al., "The Kaldi speech recognition toolkit, " in Proc. ASRU, 2011.
23. J.-F. Bonastre, N. Scheffer, D. Matrouf, C. Fredouille, A. Larcher, A. Preti, G. Pouchoulin, N. Evans, B. Fauve, and J. Mason, "ALIZE/SpkDet: A state-of-the-art open source software for speaker recognition, " in Proc. ISCA/IEEE Speaker Odyssey 2008.
24. Y. Miao, "Kaldi+PDNN: building DNN-based ASR systems with Kaldi and PDNN, " arXiv:1401.6984, 2014.
25. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, "Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, " Journal of Machine Learning Research, vol. 11, pp. 3371-3408, 2010.
26. J. Gehring, Y. Miao, F. Metze, and A. Waibel, "Extracting deep bottleneck features using stacked auto encoders, " in Proc. ICASSP, 2013.
27. J. Gehring, W. Lee, K. Kilgour, I. Lane, Y. Miao, and A. Waibel, "Modular combination of deep neural networks for acoustic modeling, " in Proc. Inter speech, pp. 94-98, 2013.
28. Y. Miao, F. Metze, and S. Rawat, "Deep maxout networks for low-resource speech recognition, " in Proc. ASRU, pp. 398-403, 2013.
29. J. Chiu, and A. Rudnicky, "Using conversational word bursts in spoken term detection, " in Proc. Inter speech, 2013.
30. Y. Miao, and F. Metze, "Improving low-resource CDDNN- HMM using dropout and multilingual DNN training, " in Proc. Inter speech, pp. 2237-2241, 2013.
31. Y. Miao, H. Zhang, and F. Metze, "Distributed learning of multilingual DNN feature extractors using GPUs, " to appear in Proc. Inter speech, 2014.
32. Y. Miao, and F. Metze, "Improving language-universal feature extraction with deep maxout and convolutional neural networks, " to appear in Proc. Inter speech, 2014.