A new method for segmenting continuous speech

Speech recognition systems are increasingly utilized in various applications like telephone services where a user places a call by uttering the digits or the name of the person. One of the main problems in this application is the segmentation of the input utterance into speech and nonspeech portions. Current approaches typically suffer from two problems. They either incorporate noise as a part of the word to be enrolled or falsely classify a portion of a word as noise. As a result, recognition performance suffers. The authors present another approach to automatically segment continuous speech and create speaker dependent models. To verify the hypothesis, they use a database of 30 speakers whose speech has been recorded over the public switched telephone network. With this database, they benchmark their algorithm against a state of the art approach and show a 4× reduction in the error rate of the recognition system