|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application
Relative Speech Emotion Recognition Based Artificial Neural Network
December 19-December 20
ISBN: 978-0-7695-3490-9
| ASCII Text | x | ||
| Liqin Fu, Xia Mao, Lijiang Chen, "Relative Speech Emotion Recognition Based Artificial Neural Network," Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE, vol. 2, pp. 140-144, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/PACIIA.2008.355, author = {Liqin Fu and Xia Mao and Lijiang Chen}, title = {Relative Speech Emotion Recognition Based Artificial Neural Network}, journal ={Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE}, volume = {2}, year = {2008}, isbn = {978-0-7695-3490-9}, pages = {140-144}, doi = {http://doi.ieeecomputersociety.org/10.1109/PACIIA.2008.355}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE TI - Relative Speech Emotion Recognition Based Artificial Neural Network SN - 978-0-7695-3490-9 SP140 EP144 A1 - Liqin Fu, A1 - Xia Mao, A1 - Lijiang Chen, PY - 2008 KW - speech emotion recognition KW - relative features KW - ANN VL - 2 JA - Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE ER - | |||
Artificial Neural Network (ANN) models based on static features vector as well as normalized temporal features vector, were used to recognize emotion state from speech. Moreover, relative features obtained by computing the changes of acoustic features of emotional speech relative to those of neutral speech were adopted to weaken the influence from the individual difference. The methods to relativize static features and temporal features were introduced individually and experiments based Germany database and Mandarin database were implemented. The results show that the performance of relative features excels that of absolute features for emotion recognition as a whole. When speaker is independent, the hybrid of relative static features vector and relative temporal features normalized vector achieves the best results.
Index Terms:
speech emotion recognition, relative features, ANN
Citation:
Liqin Fu, Xia Mao, Lijiang Chen, "Relative Speech Emotion Recognition Based Artificial Neural Network," paciia, vol. 2, pp.140-144, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008
Usage of this product signifies your acceptance of the Terms of Use.