NDLI: Automatic labeling schemes for concatenative speech synthesis

Content Provider	IEEE Xplore Digital Library
Author	Kacur, J. Cepko, J. Palenik, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Telecommun., Slovak Univ. of Technol., Bratislava (Kacur, J.; Cepko, J.; Palenik, A.)
Abstract	This article discusses problems and solutions related to the labeling of the speech which is further used in speech synthesis. Although there are several synthesis methods, here we put focus on the concatenative speech synthesis, which is especially sensitive to labeling errors. As it uses huge amount of data it must be processed automatically. It is well accepted that the most precise automatic labeling methods are based on the automatic speech recognition systems and the most successful ones are utilizing HMM technology which connects more advantages, however there are other wide-spread methods like DTW and its various combinations. In the following paper HMM algorithm using various models was trained, tested and evaluated on couple of Slovak speech databases, which had either non-specific content or constructed to cover specific tasks such as weather forecast and railway information system. The results showed that the tested complexity of HMM models do not influence the accuracy of the labeled phoneme borders considerably. However, the comparison with a sample of hand-labeled data showed that the automatic HMM methods must be manually revised to be used for high quality concatenative synthesis.
Starting Page	639
Ending Page	642
File Size	127296
Page Count	4
File Format	PDF
ISBN	9781424433643
ISSN	13342630
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-10
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	ELMAR-Croatian Society Electronics in Marine
Subject Keyword	System testing Speech analysis Weather forecasting HNM speech database Predictive models Speech synthesis Automatic speech recognition TTS Databases phonetic segmentation Hidden Markov models labeling Rail transportation Labeling
Content Type	Text
Resource Type	Article

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