NDLI: Refining Segmental Boundaries using Support Vector Machine

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Refining Segmental Boundaries using Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Namnabat, M. Homayounpour, M.M.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Eng. & Inf. Technol., Amirkabir Univ. of Technol., Tehran (Namnabat, M.; Homayounpour, M.M.)
Abstract	High accuracy phonetic segmentation is critical to achieve good quality in concatenative speech synthesis. However, the processing and inspection of a large amount of recorded speech will become a labor-intensive and error-prone job. In this paper, a post-refining method based on support vector machines (SVMs), is proposed for auto-segmentation of speech data. Our baseline system is based on a set of hidden Markov models (HMMs). This system performs forced alignment of speech data and phonemic transcription of corresponding text. A de-biasing algorithm first refines initial boundary estimates. SVM models are then used for more refinement of de-biased boundaries. Subsequently, a LBG vector quantization algorithm is used to reduce the amount of speech for training SVM models. This leads to a considerable decrease in necessary time to train SVM models. We achieved a performance of 94.3% for segmentation of phoneme boundaries with less than 15 ms deviation from hand labeled boundaries
File Size	165126
File Format	PDF
ISBN	0780397363
DOI	10.1109/ICOSP.2006.345517
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Hidden Markov models Kernel Speech synthesis Support vector machine classification Speech processing Speech recognition Speech analysis Humans Context modeling
Content Type	Text
Resource Type	Article

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