Abstract
Methods for automated vocal emotion recognition often use acoustic feature vectors that are computed for each frame in an utterance, and global statistics based on these acoustic feature vectors. However, at least two considerations argue for usage of phoneme class specific features for emotion recognition. First, there are well-known effects of phoneme class on some of these features. Second, it is plausible that emotion influences the speech signal in ways that differ between phoneme classes. A new method based on the concept of phoneme class specific features is proposed in which different features are selected for regions associated with different phoneme classes and then optimally combined, using machine learning algorithms. A small but significant improvement was found when this method was compared with an otherwise identical method in which features were used uniformly over different phoneme classes.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010 |
| Pages | 1161-1164 |
| Number of pages | 4 |
| State | Published - 2010 |
| Event | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan Duration: Sep 26 2010 → Sep 30 2010 |
Other
| Other | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 |
|---|---|
| Country | Japan |
| City | Makuhari, Chiba |
| Period | 9/26/10 → 9/30/10 |
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Keywords
- Biomedical application
- Emotion recognition
- Phoneme class specific features
ASJC Scopus subject areas
- Language and Linguistics
- Speech and Hearing
Cite this
Automated vocal emotion recognition using phoneme class specific features. / Kiss, Géza; Van Santen, Jan.
Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010. 2010. p. 1161-1164.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automated vocal emotion recognition using phoneme class specific features
AU - Kiss, Géza
AU - Van Santen, Jan
PY - 2010
Y1 - 2010
N2 - Methods for automated vocal emotion recognition often use acoustic feature vectors that are computed for each frame in an utterance, and global statistics based on these acoustic feature vectors. However, at least two considerations argue for usage of phoneme class specific features for emotion recognition. First, there are well-known effects of phoneme class on some of these features. Second, it is plausible that emotion influences the speech signal in ways that differ between phoneme classes. A new method based on the concept of phoneme class specific features is proposed in which different features are selected for regions associated with different phoneme classes and then optimally combined, using machine learning algorithms. A small but significant improvement was found when this method was compared with an otherwise identical method in which features were used uniformly over different phoneme classes.
AB - Methods for automated vocal emotion recognition often use acoustic feature vectors that are computed for each frame in an utterance, and global statistics based on these acoustic feature vectors. However, at least two considerations argue for usage of phoneme class specific features for emotion recognition. First, there are well-known effects of phoneme class on some of these features. Second, it is plausible that emotion influences the speech signal in ways that differ between phoneme classes. A new method based on the concept of phoneme class specific features is proposed in which different features are selected for regions associated with different phoneme classes and then optimally combined, using machine learning algorithms. A small but significant improvement was found when this method was compared with an otherwise identical method in which features were used uniformly over different phoneme classes.
KW - Biomedical application
KW - Emotion recognition
KW - Phoneme class specific features
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M3 - Conference contribution
SP - 1161
EP - 1164
BT - Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
ER -