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

• Type: Journal
• Year: 1999
• Where: CSL
• Authors: R. E. Donovan, Philip C. Woodland
• Keywords: Automated Reasoning, Concatenation Synthesizer, CSL 1999, Hidden Markov Models, Waveform Concatenation Synthesizer