Knowledge-based manner class segmentation based on the acoustic event and landmark detection algorithm

Jung In Lee, Jeung Yoon Choi, Hong Goo Kang

Research output: Contribution to journalArticle

Abstract

There have been steady demands for a speech segmentation method to handle various speech applications. Conventional segmentation algorithms show reliable performance but they require a sufficient training database. This letter proposes a manner class segmentation method based on the acoustic event and landmark detection used in the knowledge-based speech recognition system. Measurements of sub-band abruptness and additional parameters are used to detect the acoustic events. Candidates of manner classes are segmented from the acoustic events and determined based on the knowledge of acoustic phonetics and acoustic parameters. Manners of vowel/glide, nasal, fricative, stop burst, stop closure, and silence are segmented in this system. In total, 71% of manner classes are correctly segmented with 20-ms error boundaries.

Original languageEnglish
Pages (from-to)1682-1685
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE97-D
Issue number6
DOIs
Publication statusPublished - 2014 Jun

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Knowledge-based manner class segmentation based on the acoustic event and landmark detection algorithm'. Together they form a unique fingerprint.

  • Cite this