NDLI: Modeling segmental duration for Turkish text-to-speech

Content Provider	IEEE Xplore Digital Library
Author	Ozturk, O. Ciloglu, T.
Copyright Year	2004
Description	Author affiliation: Dokuz Eylul Univ., Izmir, Turkey (Ozturk, O.)
Abstract	Text-to-speech (TTS) synthesis can be regarded as the automatic transformation of sentences from their text form into their speech waveform by machines. The most crucial problem confronting TTS systems is the generation of natural sounding voice. In order to obtain natural sounding synthetic speech, prosodic attributes of speech such as pitch frequency, duration and intensity should be modelled appropriately. This paper summarizes the efforts to obtain duration models to be utilized in Turkish TTS systems via machine-learning algorithms. In natural speech, segment durations are highly correlated to context. Similar/same phones differ from each other in their energy, duration and fundamental frequency depending on their context. To obtain natural speech thru TTS, prosodic variations due to context should be modeled. Different methods of modeling duration have been applied over the years. Two corpus-based statistical systems - linear regression and C4.5 decision tree - are employed in modeling segment durations in Turkish.
Sponsorship	IEEE Tubitak Istanbul Teknik Universitesi Aselsan Profilo Telra TURCom SGI Datacore Divit
Starting Page	272
Ending Page	275
File Size	251704
Page Count	4
File Format	PDF
ISBN	0780383184
DOI	10.1109/SIU.2004.1338312
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-30
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear regression Natural languages Frequency Speech synthesis Decision trees Stress Context modeling
Content Type	Text
Resource Type	Article

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