Isolated word endpoint detection using time-frequency variance kernels

Alexandros Kyriakides, Costas Pitris, Alex Fink, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A major challenge in developing endpoint detection systems is the presence of background noise. We have developed a hybrid method for performing endpoint detection which is based on spectrogram estimation using LPC and a detection process based on imaging operations on the spectrogram. High-variance regions in the spectrogram, captured by variance kernels, can be used to accurately determine the endpoints of speech. This hybrid approach to endpoint detection is robust to various types and levels of background noise. Compared with two other publicly-available methods, our approach performs favorably.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
Pages1041-1045
Number of pages5
DOIs
Publication statusPublished - 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Other

Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
CountryUnited States
CityPacific Grove, CA
Period11/6/1111/9/11

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

Cite this

Kyriakides, A., Pitris, C., Fink, A., & Spanias, A. (2011). Isolated word endpoint detection using time-frequency variance kernels. In Conference Record - Asilomar Conference on Signals, Systems and Computers (pp. 1041-1045). [6190170] https://doi.org/10.1109/ACSSC.2011.6190170