Abstract
Articulatory Phonology models speech as spatio-temporal constellation of constricting events (e.g. raising tongue tip, narrowing lips etc.), known as articulatory gestures. These gestures are associated with distinct organs (lips, tongue tip, tongue body, velum and glottis) along the vocal tract. In this paper we present a Dynamic Bayesian Network based speech recognition architecture that models the articulatory gestures as hidden variables and uses them for speech recognition. Using the proposed architecture we performed: (a) word recognition experiments on the noisy data of Aurora-2 and (b) phone recognition experiments on the University of Wisconsin X-ray microbeam database. Our results indicate that the use of gestural information helps to improve the performance of the recognition system compared to the system using acoustic information only.
| Original language | English |
|---|---|
| Title of host publication | 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings |
| Pages | 131-136 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2011 Dec 1 |
| Externally published | Yes |
| Event | 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011 - Waikoloa, HI, United States Duration: 2011 Dec 11 → 2011 Dec 15 |
Publication series
| Name | 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings |
|---|
Conference
| Conference | 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011 |
|---|---|
| Country | United States |
| City | Waikoloa, HI |
| Period | 11/12/11 → 11/12/15 |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
Cite this
Robust speech recognition using articulatory gestures in a dynamic Bayesian network framework. / Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y.
2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings. 2011. p. 131-136 6163918 (2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Robust speech recognition using articulatory gestures in a dynamic Bayesian network framework
AU - Mitra, Vikramjit
AU - Nam, Hosung
AU - Espy-Wilson, Carol Y.
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Articulatory Phonology models speech as spatio-temporal constellation of constricting events (e.g. raising tongue tip, narrowing lips etc.), known as articulatory gestures. These gestures are associated with distinct organs (lips, tongue tip, tongue body, velum and glottis) along the vocal tract. In this paper we present a Dynamic Bayesian Network based speech recognition architecture that models the articulatory gestures as hidden variables and uses them for speech recognition. Using the proposed architecture we performed: (a) word recognition experiments on the noisy data of Aurora-2 and (b) phone recognition experiments on the University of Wisconsin X-ray microbeam database. Our results indicate that the use of gestural information helps to improve the performance of the recognition system compared to the system using acoustic information only.
AB - Articulatory Phonology models speech as spatio-temporal constellation of constricting events (e.g. raising tongue tip, narrowing lips etc.), known as articulatory gestures. These gestures are associated with distinct organs (lips, tongue tip, tongue body, velum and glottis) along the vocal tract. In this paper we present a Dynamic Bayesian Network based speech recognition architecture that models the articulatory gestures as hidden variables and uses them for speech recognition. Using the proposed architecture we performed: (a) word recognition experiments on the noisy data of Aurora-2 and (b) phone recognition experiments on the University of Wisconsin X-ray microbeam database. Our results indicate that the use of gestural information helps to improve the performance of the recognition system compared to the system using acoustic information only.
UR - http://www.scopus.com/inward/record.url?scp=84858964876&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858964876&partnerID=8YFLogxK
U2 - 10.1109/ASRU.2011.6163918
DO - 10.1109/ASRU.2011.6163918
M3 - Conference contribution
SN - 9781467303675
T3 - 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings
SP - 131
EP - 136
BT - 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings
ER -