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 [BibTeX] [Marc21]
Unsupervised Speech/Non-speech Detection for Automatic Speech Recognition in Meeting Rooms
Type of publication: Idiap-RR
Citation: hari-rr-06-57
Number: Idiap-RR-57-2006
Year: 2006
Institution: IDIAP
Address: Martigny, Switzerland
Abstract: The goal of this work is to provide robust and accurate speech detection for automatic speech recognition (ASR) in meeting room settings. The solution is based on computing long-term modulation spectrum, and examining specific frequency range for dominant speech components to classify speech and non-speech signals for a given audio signal. Manually segmented speech segments, short-term energy, short-term energy and zero-crossing based segmentation techniques, and a recently proposed Multi Layer Perceptron (MLP) classifier system are tested for comparison purposes. Speech recognition evaluations of the segmentation methods are performed on a standard database and tested in conditions where the signal-to-noise ratio (SNR) varies considerably, as in the cases of close-talking headset, lapel, distant microphone array output, and distant microphone. The results reveal that the proposed method is more reliable and less sensitive to mode of signal acquisition and unforeseen conditions.
Userfields: ipdinar={2006}, ipdmembership={speech}, language={English},
Keywords:
Projects Idiap
Authors Maganti, Hari Krishna
Motlicek, Petr
Gatica-Perez, Daniel
Added by: [UNK]
Total mark: 0
Attachments
  • rr06-57.pdf
  • rr06-57.ps.gz
Notes