The second 'chime' speech separation and recognition challenge: Datasets, tasks and baselines

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

Volumn , Issue , 2013, Pages 126-130

  Vincent, Emmanuel ^a   Barker, Jon ^b   Watanabe, Shinji ^c   Le Roux, Jonathan ^c   Nesta, Francesco ^d,e   Matassoni, Marco ^d  

^a INRIA   (France)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

^c MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

^d ITC IRST   (Italy)

^e Conexant Systems ^*  (United States)

Author keywords

'CHiME' Challenge; Noise robust ASR

Indexed keywords

'CHIME' CHALLENGE; AUTOMATIC SPEECH RECOGNITION; BASE-LINE PERFORMANCE; DOMESTIC ENVIRONMENTS; NOISE-ROBUST ASR; REAL-WORLD; SPEECH SEPARATION;

ELECTRICAL ENGINEERING;

SIGNAL PROCESSING;

EID: 84890541701     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6637622     Document Type: Conference Paper

References (29)

1. S. Makino, T.-W. Lee, and H. Sawada, Eds., Blind speech separation, Springer, 2007.
2. M. Wölfel and J. McDonough, Distant Speech Recognition, Wiley, 2009.
3. D. Kolossa and R. Häb-Umbach, Eds., Robust Speech Recognition of Uncertain or Missing Data- Theory and Applications, Springer, 2011.
4. J. Barker, E. Vincent, N. Ma, H. Christensen, and P. Green, "The PASCAL CHiME Speech Separation and Recognition Challenge," Computer Speech and Language, vol. 27, no. 3, pp. 621-633, 2013.
5. D. Pearce and H.-G. Hirsch, "The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proc. ICSLP, 2000, vol. 4, pp. 29-32.
6. J. McDonough, K. Kumatani, T. Gehrig, E. Stoimenov, et al., "To separate speech!: A system for recognizing simultaneous speech," in Proc. MLMI, 2007, pp. 283-294.
7. I. Himawan, I. McCowan, and M. Lincoln, "Microphone array beamforming approach to blind speech separation," in Proc. MLMI, 2007, pp. 295-305.
8. M. Cooke, J. R. Hershey, and S. J. Rennie, "Monaural Speech Separation and Recognition Challenge," Computer Speech and Language, vol. 24, pp. 94-111, 2010.
9. E. Vincent, S. Araki, F. J. Theis, G. Nolte, et al., "The Signal Separation Evaluation Campaign (2007-2010): Achievements and remaining challenges," Signal Processing, vol. 92, pp. 1928-1936, 2012.
10. M. Delcroix, K. Kinoshita, T. Nakatani, S. Araki, et al., "Speech recognition in the presence of highly nonstationary noise based on spatial, spectral and temporal speech/noise modeling combined with dynamic variance adaptation," in Proc. CHiME, 2011, pp. 12-17.
11. J. F. Gemmeke, T. Virtanen, and A. Hurmalainen, "Exemplar-based speech enhancement and its application to noise-robust automatic speech recognition," in Proc. CHiME, 2011, pp. 53-57.
12. A. Hurmalainen, K. Mahkonen, J. F. Gemmeke, and T. Virtanen, "Exemplar-based recognition of speech in highly variable noise," in Proc. CHiME, 2011, pp. 1-5.
13. H. Kallasjoki, S. Keronen, G. J. Brown, J. F. Gemmeke, et al., "Mask estimation and sparse imputation for missing data speech recognition in multisource reverberant environments," in Proc. CHiME, 2011, pp. 58-63.
14. Y.-I. Kim, H.-Y. Cho, and S.-H. Kim, "Zero-crossingbased channel attentive weighting of cepstral features for robust speech recognition: The ETRI 2011 CHiME challenge system," in Proc. Interspeech, 2011, pp. 1649-1652.
15. Z. Koldovský, J. Málek, J. Nouza, and M. Balík, "CHiME data separation based on target signal cancellation and noise masking," in Proc. CHiME, 2011, pp. 47-50.
16. D. Kolossa, R. F. Astudillo, A. Abad, S. Zeiler, et al., "CHiME Challenge: Approaches to robustness using beamforming and uncertainty-of- observation techniques," in Proc. CHiME, 2011, pp. 6-11.
17. N. Ma, J. Barker, H. Christensen, and P. Green, "Recent advances in fragment-based speech recognition in reverberant multisource environments," in Proc. CHiME, 2011, pp. 68-73.
18. R. Maas, A. Schwarz, Y. Zheng, K. Reindl, et al., "A two-channel acoustic front-end for robust automatic speech recognition in noisy and reverberant environments," in Proc. CHiME, 2011, pp. 41-46.
19. F. Nesta and M. Matassoni, "Robust automatic speech recognition through on-line semi blind source extraction," in Proc. CHiME, 2011, pp. 18-23.
20. A. Ozerov and E. Vincent, "Using the FASST source separation toolbox for noise robust speech recognition," in Proc. CHiME, 2011, pp. 86-87.
21. R. Vipperla, S. Bozonnet, D. Wang, and N. Evans, "Robust speech recognition in multi-source noise environments using convolutive non-negative matrix factorization," in Proc. CHiME, 2011, pp. 74-79.
22. F. Weninger, J. Geiger, M. Wöllmer, B. Schuller, and G. Rigoll, "The Munich 2011 CHiME challenge contribution: NMF-BLSTM speech enhancement and recognition for reverberated multisource environments," in Proc. CHiME, 2011, pp. 24-29.
23. M. P. Cooke, J. Barker, S. P. Cunningham, and X. Shao, "An audio-visual corpus for speech perception and automatic speech recognition," Journal of the Acoustical Society of America, vol. 120, pp. 2421-2424, 2006.
24. J. B. Allen and D. A. Berkley, "Image method for efficiently simulating small-room acoustics," Journal of the Acoustical Society of America, vol. 65, no. 4, pp. 943-950, 1979.
25. H. Hacihabiboǧlu, B. Günel, and A.M. Kondoz, "Headrelated transfer function filter interpolation by root displacement," in Proc. WASPAA, 2005, pp. 134-137.
26. J. Garofalo, D. Graff, D. Paul, and D. Pallett, "CSRI (WSJ0) Complete," Linguistic Data Consortium, Philadelphia, 2007.
27. S. J. Young, G. Evermann, M. J. F. Gales, T. Hain, et al., The HTK Book, version 3.4, University of Cambridge, 2006.
28. Y. Tachioka, S. Watanabe, and J. R. Hershey, "Effectiveness of discriminative training and feature transformation for reverberated and noisy speech," in Proc. ICASSP, 2013.
29. K. Vertanen, "Baseline WSJ acoustic models for HTK and Sphinx: Training recipes and recognition experiments," Tech. Rep., Cavendish Laboratory, University of Cambridge, 2006.