Statistical Pronunciation Adaptation for Spontaneous Speech Synthesis

To bring more expressiveness into text-to-speech systems, this paper presents a new pronunciation variant generation method which works by adapting standard, i. e., dictionary-based, pronunciations to a spontaneous style. Its strength and originality lie in exploiting a wide range of linguistic, articulatory and prosodic features, and in using a probabilistic machine learning framework, namely conditional random fields and phoneme-based n-gram models. Extensive experiments on the Buckeye corpus of English conversational speech demonstrate the effectiveness of the approach through objective and perceptual evaluations.