Automatic Speech Segmentation in High Noise Condition

Abstract

The accurate segmentation of speech and end points detection in adverse condition is very important for building robust automatic speech recognition (ASR) systems. Segmentation of speech is not a trivial process - in high noise conditions it is very difficult to determine weak fricatives and nasals at end of the words. An efficient threshold (a priory defined) independent speech segmentation algorithm, robust to level of disturbance signals, is developed. The results show a significant improvement of robustness of proposed algorithm with respect to traditional algorithms.

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