Title :
Emotion-detecting Based Model Selection for Emotional Speech Recognition
Author :
Pan, Y.C. ; Xu, M.X. ; Liu, L.Q. ; Jia, P.F.
Author_Institution :
Center for Speech Technology, Tsinghua National Lab for Information Science and Technology, Tsinghua University, Beijing, 100084, China. Phone: 01062796589, E-mail: panyc@cst.cs.tsinghua.edu.cn
Abstract :
As known to all, the performance of speech recognition degrades dramatically in the presence of emotion. How to deal with emotion issue properly is crucial. Most widely used approaches include robust feature extraction, speaker normalization and model tuning/retraining. In the study, a novel method is proposed, that is, adaptation technique is adopted to transform a general model into emotion-specific one with a small amount of emotion speech. Moreover, a model-selection strategy based on emotion-detection was proposed and proven to be effective, and the overall mean recognition rate increased to 80.79% with an Error Rate Reduction (ERR) of 16.55% compared to the neutral speech Acoustic Model (AM).
Keywords :
Acoustic distortion; Degradation; Emotion recognition; Loudspeakers; Phase distortion; Robustness; Speech recognition; Speech synthesis; Vocabulary; Working environment noise; adaptation; emotion-detection; emotional speech; model-selection; speech recognition;
Conference_Titel :
Computational Engineering in Systems Applications, IMACS Multiconference on
Conference_Location :
Beijing, China
Print_ISBN :
7-302-13922-9
Electronic_ISBN :
7-900718-14-1
DOI :
10.1109/CESA.2006.313485
