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 |
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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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Refinement of landmark detection and extraction of articulator-free features for knowledge-based speech recognition. / Lee, Jung In; Choi, Jeung Yoon; Kang, Hong Goo.
In: IEICE Transactions on Information and Systems, Vol. E96-D, No. 3, 03.2013, p. 746-749.Research output: Contribution to journal › Article
TY - JOUR
T1 - Refinement of landmark detection and extraction of articulator-free features for knowledge-based speech recognition
AU - Lee, Jung In
AU - Choi, Jeung Yoon
AU - Kang, Hong Goo
PY - 2013/3
Y1 - 2013/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84878248577&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878248577&partnerID=8YFLogxK
U2 - 10.1587/transinf.E96.D.746
DO - 10.1587/transinf.E96.D.746
M3 - Article
AN - SCOPUS:84878248577
VL - E96-D
SP - 746
EP - 749
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 3
ER -