Accurate location of the endpoints of an isolated word is important for reliable and robust word recognition. The endpoint detection problem is nontrivial for nonstationary backgrounds where artifacts (i.e., nonspeech events) may be introduced by the speaker, the recording environment, and the transmission system. Several techniques for the detection of the endpoints of isolated words recorded ...

In this paper we propose a speech interface function, called speech starter, that enables noise-robust endpoint (utterance) detection for speech recognition. When current speech recognizers are used in a noisy environment, a typical recognition error is caused by incorrect endpoints because their automatic detection is likely to be disturbed by non-stationary noises. The speech starter function...

We investigate two statistical-detection problems, change-point detection and pattern matching in plasma etch endpoint detection. Our approach is based on a segmental semi-Markov model framework. In the change-point detection problem, the changepoint corresponds to state switching in the model. For pattern matching, the pattern is approximated as a sequence of linear segments which are then mod...

In the study the efficiency of three features for trajectory-based endpoint detection is experimentally evaluated in the fixed-text Dynamic Time Warping (DTW) − a based speaker verification task with short phrases of telephone speech. The employed features are Modified Teager Energy (MTE), Energy-Entropy (EE) feature and Mean-Delta (MD) feature. The utterance boundaries in the endpoint detector...

The endpoint detection plays a significantly important role in the front end processing of speech recognition. It is very difficult, however, to precisely locate endpoints on the input utterance to be free on non-speech regions because of unpredictable background noise. This paper proposes a novel approach that finds robust features for better endpoint detection in a noisy incar environment. In...