Evaluating robust features on Deep Neural Networks for speech recognition in noisy and channel mismatched conditions

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

Volumn , Issue , 2014, Pages 895-899

  Mitra, Vikramjit ^a   Wang, Wen ^a   Franco, Horacio ^a   Lei, Yun ^a   Bartels, Chris ^a   Graciarena, Martin ^a  

^a SRI INTERNATIONAL   (United States)

Author keywords

Continuous speech recognition; Convolutional neural networks; Damped oscillators; Deep neural networks; Modulation features; Noise robust speech recognition

Indexed keywords

ACOUSTIC NOISE; CONTINUOUS SPEECH RECOGNITION; CONVOLUTION; NEURAL NETWORKS; SPEECH PROCESSING;

CONVOLUTIONAL NEURAL NETWORK; DAMPED OSCILLATORS; DEEP NEURAL NETWORKS; MODULATION FEATURES; NOISE ROBUST SPEECH RECOGNITION;

SPEECH COMMUNICATION;

EID: 84910075252     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. A. Mohamed, G.E. Dahl and G. Hinton, "Acoustic modeling using deep belief networks, " IEEE Trans. on ASLP, Vol. 20, no. 1, pp. 14 -22, 2012.
2. F. Seide, G. Li and D. Yu, "Conversational speech transcription using context-dependent deep neural networks, " Proc. of Interspeech, 2011.
3. B. Kingsbury, T. N. Sainath, and H. Soltau, "Scalable minimum bayes risk training of deep neural network acoustic models using distributed hessian-free optimization, " Proc. of Interspeech, 2012.
4. N. Virag, "Single channel speech enhancement based on masking properties of the human auditory system", IEEE Trans. Speech Audio Process., 7(2), pp. 126-137, 1999.
5. S. Srinivasan and D. L. Wang, "Transforming binary uncertainties for robust speech recognition", IEEE Trans Audio, Speech, Lang. Process., 15(7), pp. 2130-2140, 2007.
6. Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Adv. Front-end Feature Extraction Algorithm; Compression Algorithms, ETSI ES 202 050 Ver. 1.1.5, 2007.
7. C. Kim and R. M. Stern, "Feature extraction for robust speech recognition based on maximizing the sharpness of the power distribution and on power flooring", in Proc. ICASSP, pp. 4574- 4577, 2010.
8. V. Tyagi, "Fepstrum features: Design and application to conversational speech recognition", IBM Research Report, 11009, 2011.
9. V. Mitra, H. Franco, M. Graciarena and A. Mandal, "Normalized amplitude modulation features for large vocabulary noise-robust speech recognition", in Proc. of ICASSP, pp. 4117-4120, Japan, 2012.
10. J. G. Fiscus, "A Post-Processing System to Yield Reduced Word Error Rates: Recognizer Output Voting Error Reduction. (ROVER), " Proc. of ASRU, pp. 347-354, 1997.
11. B. Kingsbury, G. Saon, L. Mangu, M. Padmanabhan and R. Sarikaya, "Robust speech recognition in noisy environments: The 2001 IBM SPIN Eevaluation system", In Proc. of ICASSP, Vol.1, pp.I53-I56, FL, 2002.
12. S. Fine, G. Saon, and R.A. Gopinath, "Digit recognition in noisy environments via a sequential GMM/SVM system", In Proc. of ICASSP, Vol.1, pp.I49-I52, FL, 2002.
13. M. Cooke, P. Green, L. Josifovski and A. Vizinho, "Robust automatic speech recognition with missing and unreliable acoustic data", Speech Comm., 34(3), pp.267-285, 2001.
14. D. Yu, M. Seltzer, J. Li, J-T. Huang and Frank Seide, "Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks", ICLR 2013.
15. P. Zhan and A Waibel, "Vocal tract length normalization for LVCSR, " in Tech. Rep. CMU-LTI-97-150. Carnegie Mellon University, 1997.
16. V. Mitra, H. Nam, C. Espy-Wilson, E. Saltzman and L. Goldstein, "Retrieving Tract Variables from Acoustics: A comparison of different Machine Learning strategies, " IEEE Journal of Selected Topics on Signal Processing, Sp. Iss. on Statistical Learning Methods for Speech and Language Processing, Vol. 4, Iss. 6, pp. 1027-1045, 2010.
17. M. Seltzer, D. Yu, and Y. Wang, "An Investigation Of Deep Neural Networks For Noise Robust Speech Recognition", Proc of ICASSP, 2013.
18. O. Abdel-Hamid, A. Mohamed, H. Jiang, and G. Penn, "Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition, " Proc. of ICASSP, pp. 4277 -4280, 2012.
19. G. Hinton, L. Deng, D. Yu, G.E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath and B. Kingsbury, "Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, " IEEE Signal Proc. Mag., 29(6), pp.82-97, 2012.
20. O. Abdel-Hamid, L. Deng and D. Yu, "Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech Recognition, " Proc. of Interspeech, pp. 3366-3370, 2013.
21. G. Hirsch, "Experimental framework for the performance evaluation of speech recognition front-ends on a large vocabulary task", ETSI STQ-Aurora DSR Working Group, June 4, 2001.
22. V. Mitra, H. Franco and M. Graciarena, "Damped Oscillator Cepstral Coefficients for Robust Speech Recognition, " Proc. of Interspeech, pp. 886-890, 2013.
23. V. Mitra, H. Franco, M. Graciarena, and A. Mandal, "Normalized Amplitude Modulation Features for Large Vocabulary Noise-Robust Speech Recognition, " Proc. of ICASSP, pp. 4117-4120, 2012.
24. R. Drullman, J. M. Festen and R. Plomp, "Effect of Reducing Slow Temporal Modulations on Speech Reception, " J. Acoust. Soc. of Am., Vol. 95, No. 5, pp. 2670-2680, 1994.
25. O. Ghitza, "On the Upper Cutoff Frequency of Auditory Critical- Band Envelope Detectors in the Context of Speech Perception, " J. Acoust. Soc. of America, vol. 110, no. 3, pp. 1628-1640, 2001.
26. P. Maragos, J. Kaiser and T. Quatieri, "Energy Separation in Signal Modulations with Application to Speech Analysis, " IEEE Trans. Signal Processing, Vol. 41, pp. 3024-3051, 1993.
27. V. Mitra, M. McLaren, H. Franco, M. Graciarena and N. Scheffer, "Modulation Features for Noise Robust Speaker Identification, " Proc. of Interspeech, pp. 3703-3707, 2013.
28. H. Teager, "Some Observations on Oral Air Flow During Phonation, " in IEEE Trans. ASSP, pp. 599-601, 1980.
29. V. Mitra, H. Franco, M. Graciarena, D. Vergyri, "Medium duration modulation cepstral feature for robust speech recognition, " Proc. of ICASSP, Florence, 2014.
30. D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz et al., "The kaldi speech recognition toolkit, " in Proc. ASRU, 2011.
31. T. Sainath, A. Mohamed, B. Kingsbury and B. Ramabhadran, "Deep convolutional neural network for LVCSR", Proc. of ICASSP, 2013.
32. L. Deng, G. Hinton, and B. Kingsbury, "New types of deep neural network learning for speech recognition and related applications: An overview, " proc. of ICASSP, 2013.
33. Y. Ephraim and D. Malah, "Speech enhancement using a minimum mean square error log-spectral amplitude estimator, " IEEE Trans. on Acoust., Speech, Signal Processing, vol. ASSP- 33, no. 2, pp. 443-445, Apr. 1985.
34. D. Yu, L. Deng, J. Droppo, J. Wu, Y. Gong, and A. Acero, "A Minimum-mean-square-error noise reduction algorithm on melfrequency cepstra for robust speech recognition, " in Proc. of ICASSP, Las Vegas, NV, 2008.