NDLI: Selecting non-uniform units from a very large corpus for concatenative speech synthesizer

Content Provider	IEEE Xplore Digital Library
Author	Min Chu Hu Peng Hong-yun Yang Chang, E.
Copyright Year	2001
Description	Author affiliation: Microsoft Res. China, Beijing, China (Min Chu)
Abstract	This paper proposes a two-module text to speech system (TTS) structure, which bypasses the prosody model that predicts numerical prosodic parameters for synthetic speech. Instead, many instances of each basic unit from a large speech corpus are classified into categories by a classification and regression tree (CART), in which the expectation of the weighted sum of square regression error of prosodic features is used as splitting criterion. Better prosody is achieved by keeping slender diversity in prosodic features of instances belonging to the same class. A multi-tier non-uniform unit selection method is presented. It makes the best decision on unit selection by minimizing the concatenated cost of a whole utterance. Since the largest available and suitable units are selected for concatenating, distortion caused by mismatches at concatenated points is minimized. Very natural and fluent speech is synthesized, according to informal listening test.
Sponsorship	IEEE Signal Process. Soc
Starting Page	785
Ending Page	788
File Size	429013
Page Count	4
File Format	PDF
ISBN	0780370414
ISSN	15206149
DOI	10.1109/ICASSP.2001.941032
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-05-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech synthesis Synthesizers Concatenated codes Signal synthesis Signal processing Speech analysis Timbre Speech processing Predictive models Numerical models
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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