Abstract
A speech synthesis unit comprises a text processor which breaks down text into phonemes, a prosodic processor which assigns properties such as length and pitch to the phonemes based on context, and a synthesis unit which outputs an audio signal representing the sequence of phonemes according to the specified properties. The prosodic processor includes a Hidden Markov Model (HMM) to predict the durations of the phonemes. Each state of the HMM represents a duration, and the outputs are phonemes. The HMM is trained on a set of data consisting of phonemes of known identity and duration, to allow the state transition and output distributions to be calculated. The HMM can then be used for any given input sequence of phonemes to predict a most likely sequence of corresponding durations.
Published Version
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