Voice activity detection based on adjustable linear prediction and GARCH models

Speech Communication

Volumn 50, Issue 6, 2008, Pages 476-486

  Solvang, Hiroko Kato ^a,b   Ishizuka, Kentaro ^c   Fujimoto, Masakiyo ^c  

^a OSLO UNIVERSITY HOSPITAL   (Norway)

^b UNIVERSITY OF OSLO   (Norway)

^c Nippon Telegraph and Telephone Corporation   (Japan)

Author keywords

AR GARCH model; Kalman filter; Linear prediction; State space representation; Voice activity detection

Indexed keywords

ALGORITHMS; KALMAN FILTERS; LINEAR ACCELERATORS; SIGNAL TO NOISE RATIO;

LINEAR PREDICTION; STATE SPACE REPRESENTATION; VOICE ACTIVITY DETECTION (VAD);

SPEECH RECOGNITION;

EID: 44149094560     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2008.02.003     Document Type: Article

References (38)

1. Abdolahi, M., Amindavar, H., 2005. GARCH coefficients as feature for speech recognition in Persian isolated digit. In: Proc. ICASSP, Vol. 1. pp. 957-960.
2. Abramson, A., Cohen, I., 2006. Markov-switching GARCH model and application to speech enhancement in subbands. In: Proc. IWAENC. pp. 1-4.
3. Akaike H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. AC-19 (1974) 716-723
4. Akaike H. Seasonal adjustment by a Bayesian modeling. J. Time Ser. Anal. 1 1 (1980) 1-13
5. Anderson B.D.O., and Moore J.B. Optimal Filtering (1979), Prentice Hall, New Jersey
6. Basu, S., 2003. A linked-HMM model for robust voicing and speech detection. In: Proc. ICASSP, Vol. 1. pp. 816-819.
7. Bollerslev T. Generalized autoregressive conditional heteroskedasticity. J. Econometrics 51 (1986) 307-327
8. Cohen, I., 2005. Supergaussian GARCH models for speech signals. In: Proc. Interspeech. pp. 2053-2056.
9. Engle R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica 50 (1982) 987-1008
10. ETSI EN 301 708, 1999. Digital cellular telecommunications systems (Phase 2+); Voice Activity Detector (VAD) for Adaptive Multi-Rate (AMR) speech traffic channels; General description (GSM 06.94 version 7.1.1 Release 1998), V7.1.1.
11. ETSI ES 202 050, 2005. Speech Processing, Transmission and Quality Aspects (STQ); Distributed speech recognition; Advanced front-end feature extraction algorithm; Compression algorithms. V1.1.4.
12. Hirsh, H.G., Pearce, D., 2000. The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In: Proc. ISCA Tutorial and Research Workshop on Automatic Speech Recognition (ISCA ITRW ASR). pp. 181-188.
13. Ishiguro M., Kato H., and Akaike H. ARDOCK, an auto-regressive model analyzer. Computer Science Monographs Vol. 30 (1999), A publication of The Institute of Statistical Mathematics
14. Ishizuka, K., Kato, H., 2006. A feature for voice activity detection derived from speech analysis with the exponential autoregressive model. In: Proc. ICASSP, Vol. 1. pp. 789-792.
15. ITU-T Recommendation G.729, Annex B, 1996. A silence compression scheme for G.729 optimized for terminals conforming to Recommendation V.70.
16. Junqua J.-C., Mak B., and Reaves B. A robust algorithm for word boundary detection in the presence of noise. IEEE Trans. Speech Audio Process. 2 (1994) 406-412
17. Karray L., and Martin A. Towards improving speech detection robustness for speech recognition in adverse conditions. Speech Commun. 40 (2003) 261-276
18. Kitagawa G., and Gersch W. A smoothness priors-state space modeling of time series with trend and seasonality. J. Amer. Statist. Assoc. 79 (1984) 378-389
19. Kristjansson, T., Deligne, S., Olsen, P., 2005. Voicing features for robust speech detection. In: Proc. Interspeech. pp. 369-372.
20. Le Bouquin-Jeannès R., and Faucon G. Study of voice activity detector and its influence on a noise reduction system. Speech Commun. 16 (1995) 245-254
21. Lee S., and Taniguchi M. Asymptotic theory for ARCH models: LAN and residual empirical processes. Statist. Sinica 15 (2005) 215-234
22. Li K., Swamy M.N.S., and Ahmad M.O. An improved voice activity detection using higher order statistics. IEEE Trans. Speech Audio Process. 13 (2005) 965-974
23. Ling S. Adaptive estimators and tests of stationary and nonstationary short- and long-memory ARFIMA-GARCH models. J. Amer. Statist. Assoc. 98 (2003) 955-967
24. Ling S., and McAleer M. On adaptive estimation in nonstationary ARMA models with GARCH errors. Ann. Statist. 31 (2003) 642-674
25. Ljung G.M., and Box G.E.P. On a measure of lack of fit in time series models. Biometrica 68 (1978) 189-196
26. Marzinzik M., and Kollmeier B. Speech pause detection for noise spectrum estimation by tracking power envelope dynamics. IEEE Trans. Speech Audio Process. 10 (2002) 109-118
27. Nakamura, A., Matsunaga, S., Shimizu, T., Tonomura, M., Sagisaka, Y., 1998. Japanese speech database for robust speech recognition. In: Proc. ICSLP.
28. Nakamura S., Takeda K., Yamamoto K., Yamada T., Kuroiwa S., Kitaoka N., Nishiura T., Sasou A., Mizumachi M., Miyajima C., Fujimoto M., and Endo T. AURORA-2J: an evaluation framework for Japanese noisy speech recognition. IEICE Trans. Inform. Syst. E88-D (2005) 535-544
29. Nemer E., Goubran R., and Mahmoud S. Robust voice activity detection using higher-order statistics in the LPC residual domain. IEEE Trans. Speech Audio Process. 9 (2001) 217-231
30. Rabiner L.R., and Sambur M.R. An algorithm for determining the endpoints of isolated utterances. Bell Syst. Tech. J. 54 (1975) 297-315
31. Ramirez J., Segura J.C., Benitex C., de la Torre A., and Rubio A. Efficient voice activity detection algorithm using long-term speech information. Speech Commun. 42 (2004) 271-287
32. Ramírez J., and Segura J.C. Statistical voice activity detection using a multiple observation likelihood ratio test. IEEE Signal Process. Lett. 12 10 (2005) 689-692
33. Shen, J.-L., Hung, J.-W., Lee, L.-S., 1998. Robust entropy-based endpoint detection for speech recognition in noisy environments. In: Proc. ICSLP.
34. Sohn J., Kim N.S., and Sung W. A statistical model-based voice activity detection. IEEE Signal Process. Lett. 6 1 (1999) 1-3
35. Srinivasan, K., Gersho, A., 1993. Voice activity detection for cellular networks. In: Proc. IEEE Workshop on Speech Coding for Telecommunications. pp. 85-86.
36. Tahmasbi R., and Rezaei S. A soft voice activity detection using GARCH filter and variance Gamma distribution. IEEE Trans. Audio Speech Lang. Process. 15 (2007) 1129-1134
37. Tucker R. Voice activity detection using a periodicity measure. IEE Proc.-I 139 (1992) 377-380
38. West M., and Harrison P.J. Bayesian Forecasting and Dynamic Models. Springer Series in Statistics (1989), Springer-Verlag, Berlin