Refinement of Landmark Detection and Extraction of Articulator-Free Features for Knowledge-Based Speech Recognition

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概要

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• 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.

著者

• Choi Jeung-yoon

  Electrical And Electronic Engineering Yonsei University

• Kang Hong-goo

  Electrical And Electronic Engineering Yonsei University

• CHOI Jeung-Yoon

  Electrical and Electronic Engineering, Yonsei University

• LEE Jung-In

  Electrical and Electronic Engineering, Yonsei University

関連論文

• On the Importance of Transition Regions for Automatic Speaker Recognition
• Refinement of Landmark Detection and Extraction of Articulator-Free Features for Knowledge-Based Speech Recognition

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