Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Loquendo - Politecnico di Torino's 2008 NIST speaker recognition evaluation system
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Combining pulse-based features for rejecting far-field speech in a HMM-based Voice Activity Detector
Computers and Electrical Engineering
Voice activity detection based on multiple statistical models
IEEE Transactions on Signal Processing - Part I
Front-End Factor Analysis for Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
Robust Voice Activity Detection Using Long-Term Signal Variability
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Computers and Electrical Engineering
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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.