A study of voice activity detection techniques for NIST speaker recognition evaluations

Since 2008, interview-style speech has become an important part of the NIST speaker recognition evaluations (SREs). Unlike telephone speech, interview speech has lower signal-to-noise ratio, which necessitates robust voice activity detectors (VADs). This paper highlights the characteristics of interview speech files in NIST SREs and discusses the difficulties in performing speech/non-speech segmentation in these files. To overcome these difficulties, this paper proposes using speech enhancement techniques as a pre-processing step for enhancing the reliability of energy-based and statistical-model-based VADs. A decision strategy is also proposed to overcome the undesirable effects caused by impulsive signals and sinusoidal background signals. The proposed VAD is compared with the ASR transcripts provided by NIST, VAD in the ETSI-AMR Option 2 coder, satistical-model (SM) based VAD, and Gaussian mixture model (GMM) based VAD. Experimental results based on the NIST 2010 SRE dataset suggest that the proposed VAD outperforms these conventional ones whenever interview-style speech is involved. This study also demonstrates that (1) noise reduction is vital for energy-based VAD under low SNR; (2) the ASR transcripts and ETSI-AMR speech coder do not produce accurate speech and non-speech segmentations; and (3) spectral subtraction makes better use of background spectra than the likelihood-ratio tests in the SM-based VAD. The segmentation files produced by the proposed VAD can be found in http://bioinfo.eie.polyu.edu.hk/ssvad.