A Case Study on Back-End Voice Activity Detection for Distributed Specch Recognition System Using Support Vector Machines

Touazi, Azzedine; Debyeche, Mohamed

doi:10.1109/sitis.2014.54

Cited by 1 publication

(1 citation statement)

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“…In recent years, impressive results in VAD have been obtained using machine learning approaches such as deep neural network [18], deep learning [19] and support vector machine. Among these algorithms, support vector machines are the most popular classifiers that incorporate Mel frequency cepstral coefficients (MFCCs) as the discriminative feature for robust VAD development [20,21].…”

Section: Discriminative Features and Classificationmentioning

confidence: 99%

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

Ong

Tan

Vengadasalam

et al. 2017

Entropy

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Voice activity detection (VAD) is a vital process in voice communication systems to avoid unnecessary coding and transmission of noise. Most of the existing VAD algorithms continue to suffer high false alarm rates and low sensitivity when the signal-to-noise ratio (SNR) is low, at 0 dB and below. Others are developed to operate in offline mode or are impractical for implementation in actual devices due to high computational complexity. This paper proposes the upper envelope weighted entropy (UEWE) measure as a means to enable high separation of speech and non-speech segments in voice communication. The asymmetric nonlinear filter (ANF) is employed in UEWE to extract the adaptive weight factor that is subsequently used to compensate the noise effect. In addition, this paper also introduces a dual-rate adaptive nonlinear filter (DANF) with high adaptivity to rapid time-varying noise for computation of the decision threshold. Performance comparison with standard and recent VADs shows that the proposed algorithm is superior especially in real-time practical applications.

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