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This paper describes the current status of the IBM Train-able Speech Synthesis System. The system is a state-of-the-art, trainable, unit-selection based concatenative speech synthesiser. The system uses hidden Markov models (HMMs) to provide a phonetic transcription and HMM state alignment of a database of single-speaker continuous-speech training data. The(More)
One issue arising in text-to-phone conversion is inconsistency between its output and the phonetic time-alignment of the dataset, hindering the back-end's ability to access the best units to synthesize a text. Some such inconsistency is inevitable because dataset labeling requires allowance for alternate pronunciations of words, while the front-end(More)
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