Refinement of landmark detection and extraction of articulator-free features for knowledge-based speech recognition

Jung In Lee, Jeung Yoon Choi, Hong Goo Kang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Refinement methods for landmark detection and extraction of articulator-free features for a knowledge-based speech recognition system are described. Sub-band energy difference profiles are used to detect landmarks, with additional parameters used to improve accuracy. For articulator-free feature extraction, duration, relative energy, and silence detection are additionally used to find [continuant] and [strident] features. Vowel, obstruent and sonorant consonant landmarks, and locations of voicing onsets and offsets are detected within a unified framework with 85% accuracy overall. Additionally, 75% and 79% of [continuant] and [strident] features, respectively, are detected from landmarks.

Original languageEnglish
Pages (from-to)746-749
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE96-D
Issue number3
DOIs
Publication statusPublished - 2013 Mar

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 'Refinement of landmark detection and extraction of articulator-free features for knowledge-based speech recognition'. Together they form a unique fingerprint.

  • Cite this