Voice activity detection based on a family of parametric distributions
Pattern Recognition Letters
Improved Likelihood Ratio Test Detector Using a Jointly Gaussian Probability Distribution Function
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Voice activity detection based on statistical models and machine learning approaches
Computer Speech and Language
An efficient VAD based on a hang-over scheme and a likelihood ratio test
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
An efficient VAD based on a generalized gaussian PDF
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
VAD Based on Kernel Smoothed Function of EGARCH Models
Wireless Personal Communications: An International Journal
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This paper presents the behavioural mechanism of a statistical model-based voice activity detector (VAD), featuring a likelihood ratio test for the activity decision. From investigation of the VAD, it is found that detection errors could occur frequently at speech offset regions because of the delay term in the decision-directed parameter estimator, employed for the estimation of an unknown parameter of the likelihood ratio. Hence, this paper proposes a smoothed likelihood ratio so as to alleviate the detection errors at the offset region. Objective test results show that the proposed scheme is useful for achieving a considerable performance improvement for the VAD. Additionally, the proposed VAD gives detection performances superior to G.729B VAD and comparable with the AMR VAD option 2.