A simple but efficient real-time voice activity detection algorithm

European Signal Processing Conference

Volumn , Issue , 2009, Pages 2549-2553

  Moattar, M H ^a   Homayounpour, M M ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION AREA; AUDIO PROCESSING; FRONT-END PROCESSING; NOISE CONDITIONS; ONLINE PROCESSING; PARAMETER-TUNING; PROCESSING METHOD; REAL APPLICATIONS; REAL-TIME VOICE; SPECTRAL FLATNESS; SPEECH CORPORA; VOICE ACTIVITY DETECTION; VOICE ACTIVITY DETECTORS;

ALGORITHMS; PROCESSING; SIGNAL PROCESSING;

SPEECH RECOGNITION;

EID: 78049393940     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. A. Benyassine, E. Shlomot, H. Y. Su, D. Massaloux, C. Lamblin and J. P. Petit, "ITU-T Recommendation G.729 Annex B: a silence compression scheme for use with G.729 optimized for V.70 digital simultaneous voice and data applications," IEEE Communications Magazine 35, pp. 64-73, 1997.
2. M. H. Savoji, "A robust algorithm for accurate end pointing of speech," Speech Communication, pp. 45-60, 1989.
3. B. Kingsbury, G. Saon, L. Mangu, M. Padmanabhan and R. Sarikaya, "Robust speech recognition in noisy environments: The 2001 IBM SPINE evaluation system," Proc. ICASSP, 1, pp. 53-56, 2002.
4. T. Kristjansson, S. Deligne and P. Olsen, "Voicing features for robust speech detection," Proc. Interspeech, pp. 369-372, 2005.
5. R. E. Yantorno, K. L. Krishnamachari and J. M. Lovekin, "The spectral autocorrelation peak valley ratio (SAPVR) - A usable speech measure employed as a co-channel detection system," Proc. IEEE Int. Workshop Intell. Signal Process. 2001.
6. M. Marzinzik and B. Kollmeier, "Speech pause detection for noise spectrum estimation by tracking power envelope dynamics," IEEE Trans. Speech Audio Process, 10, pp. 109-118, 2002.
7. ETSI standard document, ETSI ES 202 050 V 1.1.3., 2003.
8. K. Li, N. S. Swamy and M. O. Ahmad, "An improved voice activity detection using higher order statistics," IEEE Trans. Speech Audio Process., 13, pp. 965-974, 2005.
9. W. H. Shin, "Speech/non-speech classification using multiple features for robust endpoint detection," ICASSP, 2000.
10. G. D. Wuand and C. T. Lin, "Word boundary detection with mel scale frequency bank in noisy environment," IEEE Trans. Speechand Audio Processing, 2000.
11. A. Lee, K. Nakamura, R. Nisimura, H. Saruwatari and K. Shikano, "Noise robust real world spoken dialogue system using GMM based rejection of unintended inputs," Interspeech, pp. 173-176, 2004.
12. J. Sohn, N. S. Kim and W. Sung, "A statistical model-based voice activity detection," IEEE Signal Process. Lett., pp. 1-3, 1999.
13. B. Lee and M. Hasegawa-Johnson, "Minimum Mean Squared Error A Posteriori Estimation of High Variance Vehicular Noise," in Proc. Biennial on DSP for In-Vehicle and Mobile Systems, Istanbul, Turkey, June 2007.
14. ETSI EN 301 708 recommendations, "Voice activity detector for adaptive multi-rate (AMR) speech traffic channels," 1999.
15. ETSI ES 202 050 recommendation, "Speech processing, transmission and quality aspects (STQ); distributed speech recognition; advanced front-end feature extraction algorithm; compression algorithms," 2002.
16. J. S. Garofalo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, D. S. Pallett and N. L. Dahlgren, The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus, Linguistic Data Consortium, 1993.
17. M. Bijankhan, Great Farsdat Database, Technical report, Research center on Inteligent Signal Proccessing, 2002
18. P.A. Estévez, N. Becerra-Yoma, N. Boric and J.A. Ramirez, "Genetic programming-based voice activity detection," Electronics Letters, Vol. 41, No. 20, 2005.