Voice activity detection based on deep belief networks using likelihood ratio

Abstract

A novel technique is proposed to improve the performance of voice activity detection (VAD) by using deep belief networks (DBN) with a likelihood ratio (LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function (PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.

Keywords

voice activity detection / likelihood ratio / deep belief networks

References

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