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 language | English |
|---|---|
| Pages (from-to) | 746-749 |
| Number of pages | 4 |
| Journal | IEICE Transactions on Information and Systems |
| Volume | E96-D |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2013 Mar |
All Science Journal Classification (ASJC) codes
- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence
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Lee, J. I., Choi, J. Y., & Kang, H. G. (2013). Refinement of landmark detection and extraction of articulator-free features for knowledge-based speech recognition. IEICE Transactions on Information and Systems, E96-D(3), 746-749. https://doi.org/10.1587/transinf.E96.D.746