Voice activity detection in complex noise environment
GUO Wu, MA Xiaokong
National Engineering Laboratory for Speech and Language Information Processing, School of Science and Technology, University of Science and Technology of China, Hefei 230027, China
Abstract：A voice activity detection (VAD) algorithm was developed for robust voice detection in complex noise conditions. The energy, the most dominant component and the spectral entropy are used to form three dimensional features that have been demonstrated to strongly complement each of them in the presence of complex noise. The K-mean algorithm is used to adaptively select the feature and to calculate the utterance dependent thresholds, which are applied in the following speech detection process. Tests on the NIST SRE 2008 and 2012 corpus show that this algorithm gives better performance for different noise conditions and is more robust and efficient than conventional unsupervised and supervised methods.
Alam J, Kenny P, Ouellet P, et al. Supervised/unsupervised voice activity detectors for text dependent speaker recognition on the RSR2015 corpus[C]//Proc of Speaker Odyssey 2014, Joensuu, Finland, 2014:123-130.
[2]
Ferrer L, McLaren M, Scheffer N, et al. A Noise-robust system for NIST 2012 speaker recognition evaluation[C]//Proc of Interspeech 2013, Lyon, France:International Speech and Communication Association, 2013:1981-1984.
[3]
Colibro D, Vair C, Farrell K, et al. Nuance-Politecnico di Torino's 2012 NIST speaker recognition evaluation system[C]//Proc of Interspeech 2013, Lyon, France:International Speech and Communication Association, 2013:1996-2000.
[4]
Lamel L, Rabiner LR, Rosenberg A, et al. An improved endpoint detector for isolated word recognition[J]. IEEE Trans on Acoustics, Speech, and Signal processing, 1981, 29(4):777-785.
[5]
Morales-Cordovilla J A, Ma N, Sanchez V, et al. A pitch based noise estimation technique for robust speech recognition with missing data[C]//Proc of ICASSP 2011, Prague, Czech republic:Institute of Electrical and Electronics Engineers Inc, 2011:4808-4811.
[6]
Renevey P, Drygajlo A. Entropy based voice activity detection in very noisy conditions[C]//Proc of Eurospeech 2001, Cape Town, South Africa:Institute of Electrical and Electronics Engineers Inc, 2001:1887-1890.
[7]
Moattar MH, Homayounpour M M. A simple but efficient real-time voice activity detection algorithm[C]//Proc of EUSIPCO 2009, Glasgow, United Kingdom:European Signal Processing Conference, EUSIPCO, 2009:2549-2553.
[8]
Li Q, Zheng J, Tsai A. et al. Robust endpoint detection and energy normalization for real-time speech and speaker recognition[J]. IEEE Trans on Speech & Audio Processing, 2002, 10(3):146-157.
[9]
Kinnunen T, Rajan, P. A practical, self-adaptive voice activity detector for speaker verification with noisy telephone and microphone data[C]//Proc of ICASSP 2013, Vancouver, BC, Canada:Institute of Electrical and Electronics Engineers Inc, 2013:7229-7233.
[10]
Yu H B, Mak M W. Comparison of voice activity detectors for interview speech in NIST speaker recognition evaluation[C]//Proc of Interspeech 2011, Florence, Italy:International Speech and Communication Association, 2011:7229-7233.
[11]
NIST. The NIST year 2008 speaker recognition evaluation plan[EB/OL].[2008-04-02]. http://www.itl.nist.gov/iad/mig/tests/sre/2008/sre08_evalplan_release4.pdf.
[12]
NIST. The NIST Year 2012 Speaker Recognition Evaluation Plan[EB/OL].[2012-05-30]. http://www.nist.gov/itl/iad/mig/upload/NIST_SRE12_evalplan-v17-r1.pdf.
[13]
Guo W, Long Y H, Li Y J, et al. iFLY system for the NIST 2008 speaker recognition evaluation[C]//Proc of ICASSP 2009, Taipei, China:Institute of Electrical and Electronics Engineers Inc, 2009:4209-4212.
[14]
Rahim S, Lee K A, Tomi K, et al. I4U submission to NIST SRE 2012:A large-scale collaborative effort for noise-robust speaker verification[C]//Proc of Interspeech 2013, Lyon, France:International Speech and Communication Association, 2013:1986-1990.
2016, Vol. 56 
