A Survey and Evaluation of Voice Activity Detection Algorithms
Abstract: The term Voice Activity Detector (VAD) refers to a class of signal processing methods that detects if short segments of a speech signal contain voiced or unvoiced signal data. A VAD is normally using decision rules based on selected estimated signal features. VADs play a major role as a preprocessing block in a variety of speech processing applications such as speech enhancement, speech coding and speech recognition where it is desirable to classify voiced signal parts from unvoiced. This thesis presents a thorough investigation of modern VAD algorithms that are based on energy threshold, zero crossing and other statistical measures. The selected VAD algorithms are implemented in MATLAB and evaluated using objective parameters in different noise environments. The simulation results indicate that the selected methods produce favorable results in the noise environments with SNR above 5dB. VAD based on pattern recognition approach method proved effective when compared to those based on energy threshold, zero crossing measures and statistical measures.
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