Phone-duration-dependent long-term dynamic features for a stochastic model-based voice activity detection

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

Volumn , Issue , 2008, Pages 1293-1296

  Fukuda, Takashi ^a   Ichikawa, Osamu ^a   Nishimura, Masafumi ^a  

^a IBM JAPAN LTD   (Japan)

Author keywords

Average phoneme duration; Dynamic feature; Long term temporal information; Voice activity detection

Indexed keywords

AUTOMATIC SPEECH RECOGNITION SYSTEM; AVERAGE PHONEME DURATION; CEPSTRUM; CONVENTIONAL METHODS; DYNAMIC FEATURE; DYNAMIC FEATURES; ERROR REDUCTION; FEATURE PARAMETERS; LONG TERM DYNAMICS; LOW SNR; NOISE ROBUSTNESS; TEMPORAL INFORMATION; VOICE ACTIVITY DETECTION;

FEATURE EXTRACTION; SPEECH COMMUNICATION; STOCHASTIC MODELS;

SPEECH RECOGNITION;

EID: 84867218934     PISSN: None     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. S. V. Gerven and F. Xie, "A comparative study of speech detection methods," Proc. Eurospeech '97, vol. III, pp.1095-1098 (1997).
2. J. Sohn, N. S. Kim, W. Sung, "A statistical model-based voice activity detection," IEEE Signal Processing Letters, Vol. 6, pp. 1-3 (1999).
3. Y. D. Cho and A. Kondoz, "Analysis and improvement of a statistical model-based voice activity detector," IEEE Signal Processing Letters, Vol. 8, No. 10, pp.276-278 (2001).
4. A. Martin, D. Charlet, and M. Manuuary, "Robust speech / nonspeech detection using LDA applied to MFCC" Proc. ICASSP '01, vol. I, pp.237-240 (2001).
5. K. Li, M. N. S. Swamy, and M. O. Ahmad, "An improved voice activity detection using higher order statistics," IEEE Trans. Speech and Audio Processing, Vol. 13, No. 5, pp. 965-974 (2005).
6. K. Yamamoto, F. Jabloun, K. Reinhard, and A. Kawamura, "Robust endpoint detection for speech recognition based on discriminative feature extraction," Proc. ICASSP '06, vol. I, pp.805-808 (2006).
7. H. Hermansky and S. Sharma, "TRAPS - Classifiers of Temporal Patterns", Proc. ICASSP '99, Vol. I, pp. 289-292 (1999).
8. B. Chen, Q. Zhu, and N. Morgan, "Learning long-term temporal features in LVCSR using neural networks", Proc. ICSLP, pp. 612-615 (2004).
9. J. Ramirez, J. C. Segura, C. Benitez, A. Torre, and A. Rubio, "Efficient voice activity detection algorithms using long-term speech information," Speech Communication, Vol. 42, pp. 271-287 (2004).
10. D. Poeppel, "The analysis of speech in different temporal integration windows: cerebral lateralization as asymmetric sampling in time," Speech Communication, Vol. 41, pp. 245-255 (2003).
11. R. Drullman, J. M. Festen, and R. Plomp, "Effect of temporal envelope smearing on speech perception," J. Acoust. Soc. Amer., Vol. 95, pp. 1053-1064 (1994).
12. R. Drullman, J. M. Festen, and R. Plomp, "Effect of reducing slow temporal modulations on speech perception," J. Acoust. Soc. Amer., Vol. 95, pp. 2670-2680 (1994).
13. N. Kanedera, H. Hermansky, and T. Arai, "Desired characteristics of modulation spectrum for robust automatic speech recognition," Proc. ICASSP'98, pp.613-616 (1998).
14. T. Fukuda, O. Ichikawa, and M. Nishimura, "Short- and Long-term Dynamic Features for Robust Speech Recognition," Interspeech 2008.
15. N. Kitaoka et. al, "Development of evaluation framework for voice activity detection under noisy environment," IPSJ sig. technical reports, 2006-SLP-63, pp. 1-6 (2006), in Japanese.
16. ITU-T recommendation G.729-Annex B, "A silence compression scheme for G.729 optimized for terminals conforming to recommendation V.70," (1996).
17. ETSI ES 202 050 recommendation, "Speech processing, transmission and quality aspects (STQ); distributed speech recognition; advanced front-end feature extraction algorithm; compression algorithm" (2002).