Abstract
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.
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Notes
- 1.
CRFs allow dependencies between predicted phonemes but it appeared in preliminary work that using a separate phonological model is better to avoid overfitting the training data.
- 2.
Binomial test with \(\alpha =0.1\) and votes for “No preference” equally spread over A and B, following the methodology proposed in [23].
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Acknowledgments
This study has been realized under the ANR (French National Research Agency) project SynPaFlex ANR-15-CE23-0015.
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Qader, R., Lecorvé, G., Lolive, D., Tahon, M., Sébillot, P. (2017). Statistical Pronunciation Adaptation for Spontaneous Speech Synthesis. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_11
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