Accès libre

Telephone Speech Endpoint Detection using Mean-Delta Feature

   | 15 juil. 2014
À propos de cet article

Citez

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 are provided by means of state automaton and a set of thresholds based only on trajectory characteristics. The training and testing have been done with noisy telephone speech (short phrases in Bulgarian language with length of about 2 s) selected from BG-SRDat corpus. The results of the experiments have shown that the MD feature demonstrates the best performance in the endpoint detection tests in terms of the verification rate.

eISSN:
1314-4081
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Computer Sciences, Information Technology