Non-negative matrix deconvolution in noise robust speech recognition

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

Volumn , Issue , 2011, Pages 4588-4591

  Hurmalainen, Antti ^a   Gemmeke, Jort ^b   Virtanen, Tuomas ^a  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^b RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

Automatic speech recognition; deconvolution; exemplar based; noise robustness; sparsity

Indexed keywords

AUTOMATIC SPEECH RECOGNITION; CLEAN SPEECH; CONVOLUTIVE MODELS; EXEMPLAR-BASED; HIGH NOISE; NOISE ROBUST SPEECH RECOGNITION; NOISE ROBUSTNESS; NON-NEGATIVE MATRIX; OVERLAPPING WINDOW; RECOGNITION RATES; RECOGNITION SYSTEMS; SHIFT INVARIANT; SPARSITY; SPECTRAL PATTERNS; SPECTROGRAMS; TEMPORAL ALIGNMENT; TEMPORAL POSITION;

ACOUSTIC NOISE; SIGNAL PROCESSING; SPEECH COMMUNICATION; SPEECH PROCESSING;

SPEECH RECOGNITION;

EID: 80051620372     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5947376     Document Type: Conference Paper

References (14)

1. A. Acero, L. Deng, T. Kristjansson, and J. Zhang, "HMM adaptation using vector Taylor series for noisy speech recognition," in Proceedings of ICSLP, 2000, pp. 869-872.
2. H. Van hamme, "Robust speech recognition using cepstral domain missing data techniques and noisy masks," in Proceedings of ICASSP, 2004, pp. 213-216.
3. B. Raj and R.M. Stern, "Missing-feature approaches in speech recognition," IEEE Signal Processing Magazine, vol. 22, no. 5, pp. 101-116, September 2005.
4. J.F. Gemmeke, T. Virtanen, and A. Hurmalainen, "Exemplar-based sparse representations for noise robust automatic speech recognition," Accepted for publication in IEEE Transactions on Audio, Speech, and Language Processing, 2011.
5. T. Virtanen, J.F. Gemmeke, and A. Hurmalainen, "State-based labelling for a sparse representation of speech and its application to robust speech recognition," in Proceedings of INTERSPEECH, 2010.
6. J. Gemmeke, L. ten Bosch, L. Boves, and B. Cranen, "Using sparse representations for exemplar based continuous digit recognition," in Proceedings of EUSIPCO, 2009, pp. 1755-1759.
7. T. Virtanen, "Monaural sound source separation by non-negative matrix factorization with temporal continuity and sparseness criteria," IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 3, 2007.
8. P. Smaragdis, "Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs," in Independent Component Analysis and Blind Signal Separation, pp. 494-499. 2004.
9. T. Virtanen, "Separation of sound sources by convolutive sparse coding," in Proceedings of ISCA Tutorial and Research Workshop on Statistical and Perceptual Audio Processing, 2004.
10. P.D. O'Grady and B.A. Pearlmutter, "Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint," Neurocomputing, pp. 88-101, 2008.
11. P. Smaragdis, "Convolutive speech bases and their application to supervised speech separation," IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 1, 2007.
12. T. Virtanen, Sound source separation in monaural music signals, Ph.D. thesis, Tampere University of Technology, 2006.
13. H. Hirsch and D. Pearce, "The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proceedings of ISCA Tutorial and Research Workshop, 2000, pp. 181-188.
14. J.F. Gemmeke and T. Virtanen, "Artificial and online acquired noise dictionaries for noise robust ASR," in Proceedings of INTERSPEECH, 2010.