Affiliation:
1. Wire Communications Laboratory, Department of Electrical & Computer Engineering, University of Patras, Rion-Patras 26500, Greece
Abstract
In the present work we address the problem of phonetic segmentation of emotional speech. Investigating various traditional and recent HMM-based methods for speech segmentation, which we elaborated for the specifics of emotional speech segmentation, we demonstrate that the HMM-based method with hybrid embedded-isolated training offers advantageous segmentation accuracy, when compared to other HMM-based models used so far. The increased precision of the segmentation is a consequence of the iterative training process employed in the hybrid-training method, which refines the model parameters and the estimated phonetic boundaries taking advantage of the estimations made at previous iterations. Furthermore, we demonstrate the benefits of using purposely-built models for each target category of emotional speech, when compared to the case of one common model built solely from neutral speech. This advantage, in terms of segmentation accuracy, justifies the effort for creating and employing the purposely-built segmentation models per emotion category, since it significantly improves the overall segmentation accuracy.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献