Abstract
In this article, we present a new voice activity detection (VAD) algorithm that is based on statistical models and empirical rule-based energy detection algorithm. Specifically, it needs two steps to separate speech segments from background noise. For the first step, the VAD detects possible speech endpoints efficiently using the empirical rule-based energy detection algorithm. However, the possible endpoints are not accurate enough when the signal-to-noise ratio is low. Therefore, for the second step, we propose a new gaussian mixture model-based multiple-observation log likelihood ratio algorithm to align the endpoints to their optimal positions. Several experiments are conducted to evaluate the proposed VAD on both accuracy and efficiency. The results show that it could achieve better performance than the six referenced VADs in various noise scenarios.
Highlights
Voice activity detector (VAD) segregates speeches from background noise
We propose a new voice activity detection (VAD) algorithm by combining the empirical rulebased energy detection algorithm and the statistical models together
The results show that the proposed algorithm could achieve better performances than the six existing algorithms in various noise scenarios at different signal-to-noise ratio (SNR) levels
Summary
Voice activity detector (VAD) segregates speeches from background noise. It finds diverse applications in many modern speech communication systems, such as speech recognition, speech coding, noisy speech enhancement, mobile telephony, and very small aperture terminals. During the past few decades, researchers have tried many approaches to improve the VAD performance. Traditional approaches include energy in time domain [1,2], pitch detection [3], and zero-crossing rate [2,4]. Several spectral energy-based new features were proposed, including energy-entropy feature [5], spacial signal correlation [6], cepstral feature [7], higherorder statistics [8,9], teager energy [10], spectral divergence [11], etc. Multi-band technique, which utilized the band differences between the speech and the noise, was employed to construct the features [12,13]
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