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CSL
1999
Springer

A hidden Markov-model-based trainable speech synthesizer

9 years 11 months ago
A hidden Markov-model-based trainable speech synthesizer
This paper presents a new approach to speech synthesis in which a set of cross-word decision-tree state-clustered context-dependent hidden Markov models are used to define a set of subphone units to be used in a concatenation synthesizer. The models, trees, waveform segments and other parameters representing each clustered state are obtained completely automatically through training on a 1 hour single-speaker continuous-speech database. During synthesis the required utterance, specified as a string of words of known phonetic pronounciation, is generated as a sequence of these clustered states using a TD-PSOLA waveform concatenation synthesizer. The system produces speech which, though in a monotone, is both natural sounding and highly intelligible. A Modified Rhyme Test conducted to measure segmental intelligibility yielded a 5
R. E. Donovan, Philip C. Woodland
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1999
Where CSL
Authors R. E. Donovan, Philip C. Woodland
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