State-based labelling for a sparse representation of speech and its application to robust speech recognition

Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010

Volumn , Issue , 2010, Pages 893-896

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

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^b RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

Automatic speech recognition; Noise robustness; Sparse representations

Indexed keywords

ACOUSTIC NOISE; SPEECH; SPEECH COMMUNICATION; SPEECH RECOGNITION;

AUTOMATIC SPEECH RECOGNITION; LINEAR COMBINATIONS; NOISE ROBUSTNESS; NOISE-ROBUST AUTOMATIC SPEECH RECOGNITION; ROBUST SPEECH RECOGNITION; SPARSE REPRESENTATION; STATE TRANSITIONS; TRAINING DATABASE; ITS APPLICATIONS; LABELINGS; NOISY SPEECH; SPEECH APPLICATIONS; STATE BASED;

SPEECH RECOGNITION; SPEECH COMMUNICATION;

EID: 79959837544     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. J. F. Gemmeke and T. Virtanen, "Noise robust exemplar-based connected digit recognition," in Proc. of IEEE Int. Conf. on Audio, Speech and Signal Processing, Dallas, USA, 2010.
2. B. King and L. Atlas, "Single-channel source separation using simplified-training complex matrix factorization," in Proc. of IEEE Int. Conf. on Audio, Speech and Signal Processing, Dallas, USA, 2010.
3. G. S. V. S. Sivaram, S. K. Nemala, M. Elhilali, T. D. Tran, and H. Hermansky, "Sparse coding for speech recognition," in Proc. of IEEE Int. Conf. on Audio, Speech and Signal Processing, Dallas, USA, 2010.
4. T. N. Sainath, A. Carmi, D. Kanevsky, and B. Ramabhadran, "Bayesian compressive sensing for phonetic classification," in Proc. of IEEE Int. Conf. on Audio, Speech and Signal Processing, Dallas, USA, 2010.
5. M. Van Segboeck and H. Van hamme, "Unsupervised learning of time-frequency patches as a noise-robust representation of speech," Speech Communication, vol. 51, no. 11, 2009.
6. B. Schuller, F. Weninger, M. Wllmer, Y. Sun, and G. Rigoll, "Nonnegative matrix factorization as noise-robust feature extractor for speech recognition," in Proc. of IEEE Int. Conf. on Audio, Speech and Signal Processing, Dallas, USA, 2010.
7. J. Gemmeke, H. Van hamme, B. Cranen, and L. Boves, "Compressive sensing for missing data imputation in noise robust speech recognition," IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 2, pp. 272-287, 2010.
8. 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.
9. H. Van hamme, "Robust speech recognition using cepstral domain missing data techniques and noisy masks," in Proc. ICASSP, Montreal, Quebec, Canada, May 17-21 2004, pp. 213-216.
10. H. Hirsch and D. Pearce, "The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proc. of ISCA ASR2000 Workshop, Paris, France, 2000, pp. 181-188.
11. M. Van Segbroeck and H. Van hamme, "Robust speech recognition using missing data techniqies in the prospect domain and fuzzy masks," in Proc. of IEEE Int. Conf. on Audio, Speech and Signal Processing, Las Vegas, USA, 2008.
12. 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.