An isolated speech endpoint detector using multiple speech features

Abstract

Energy and zero crossing rate of the speech signal have been the two most widely used features for detecting the endpoints of an utterance. This paper proposed a new approach for locating the endpoint for isolated speech, which significantly improve the endpoint detector performance. The proposed algorithm relies on multiple speech features: root mean square energy (rmse), zero crossing rate (zcr) and cepstral coefficient (cepstrum) where the Euclidean distance measure is adopted to accurately detect the endpoint of an isolated utterance. This algorithm offers better performance than conventional algorithm which using energy only. The vocabulary for the experiment includes English digit from 1 to 9. These experimental results were conducted by 360 utterances from a male speaker. Experimental results show that the accuracy of the algorithm is quite acceptable.