MAP-based perceptual modeling for noisy speech recognition

Yung Ji Sher, Yeou Jiunn Chen, Yu Hsien Chiu, Kao Chi Chung, C. H. Wu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This study presents a maximum a posteriori (MAP) based perceptual modeling approach to deal with the issue of recognition degradation in noisy environment. In this approach, MAP-based noise detection is first applied to identify the noise segment in an utterance. Subtractive-type enhancement algorithm with masking properties of the human auditory system is then used to reduce the noise effect. Finally, MAP-based incremental noise model adaptation is developed to overcome the model inconsistencies between training and testing environments. For performance evaluation of the proposed approach, a Mandarin keyword recognition system was constructed. The experimental results show that the proposed approach achieves a better recognition rate compared to the audible noise suppression (ANS) and parallel model combination (PMC) methods.

Original languageEnglish
Pages (from-to)999-1013
Number of pages15
JournalJournal of Information Science and Engineering
Volume22
Issue number5
Publication statusPublished - 2006 Sep

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics

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