An evolutionary approach for segmentation of noisy speech signals for efficient voice activity detection

Farook Sattar, Frank Rudzicz, Moe Pwint


This paper presents a new approach to automatically segmenting speech signals in noisy environments. Segmentation of speech signals is formulated as an optimization problem and the boundaries of the speech segments are detected using a genetic algorithm (GA). The number of segments present in a signal is initially estimated from the reconstructed sequence of the original signal using the minimal number of Walsh basis functions. A multi-population GA is then employed to determine the locations of segment boundaries. The segmentation results are improved through the generations by introducing a new evaluation function which is based on the sample entropy and a heterogeneity measure. Experimental results show that the proposed approach can accurately detect the noisy speech segments as well as noise-only segments under various noisy conditions.

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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