Abstract
The performance of speaker recognition system is easily disturbed by the changes of the internal states of human. The ongoing work proposes an approach of speech emotion-state conversion to improve the performance of speaker identification system over various affective speech. The features of neutral speech are modified according to statistical prosodic parameters of emotion utterances. Speaker models are generated based on the converted speech. The experiments conducted on an emotion corpus with 14 emotion states shows promising results with an improved performance by 7.2%. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Li, D., Yang, Y., Wu, Z., & Wu, T. (2005). Emotion-state conversion for speaker recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3784 LNCS, pp. 403–410). Springer Verlag. https://doi.org/10.1007/11573548_52
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
