Soft missing-feature mask generation for simultaneous speech recognition system in robots

Toru Takahashi, Shun'ichi Yamamoto, Kazuhiro Nakadai, Kazunori Komatani, Tetsuya Ogata, Hiroshi G. Okuno

Research output: Contribution to journalConference article

7 Citations (Scopus)

Abstract

This paper addresses automatic soft missing-feature mask (MFM) generation based on a leak energy estimation for a simultaneous speech recognition system. An MFM is used as a weight for probability calculation in a recognition process. In a previous work, a threshold-base-zero-or-one function was applied to decide if spectral parameter can be reliable or not for each frequency bin. The function is extended into a weighted sigmoid function which has two free parameters. In addition, a contribution ratio of static features is introduced for the probability calculation in a recognition process which static and dynamic features are input. The ratio can be implemented as a part of soft mask. The average recognition rate based on a soft MFM improved by about 5% for all directions from a conventional system based on a hard MFM. Word recognition rates improved from 70 to 80% for peripheral talkers and from 93 to 97% for front speech when speakers were 90 degrees apart.

Original languageEnglish
Pages (from-to)992-995
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2008 Dec 1
Externally publishedYes
EventINTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia
Duration: 2008 Sep 222008 Sep 26

Keywords

  • Missing feature theory
  • Robot audition
  • Simultaneous speech recognition
  • Soft mask
  • Speech recognition

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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