Adaptation de la prononciation pour la synthèse de la parole spontanée en utilisant des informations linguistiques

Raheel Qader 1 Gwénolé Lecorvé 1 Damien Lolive 1 Pascale Sébillot 2
1 EXPRESSION - Expressiveness in Human Centered Data/Media
UBS - Université de Bretagne Sud, IRISA-D6 - MEDIA ET INTERACTIONS
2 LinkMedia - Creating and exploiting explicit links between multimedia fragments
IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
Abstract : This paper presents a new pronunciation adaptation method which adapts canonical pronunciations to a spontaneous style. This is a key task in text-to-speech as those pronunciation variants bring expressiveness to synthetic speech, thus enabling new potential applications. The strength of the method is to solely rely on linguistic features and to consider a probabilistic machine learning framework, namely conditional random fields, to produce the adapted pronunciations. Features are selected in a first series of experiments, then combined in the backend experiments. Results on the Buckeye conversational English speech corpus show that adapted pronunciations significantly better reflect spontaneous speech than canonical ones.
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Raheel Qader, Gwénolé Lecorvé, Damien Lolive, Pascale Sébillot. Adaptation de la prononciation pour la synthèse de la parole spontanée en utilisant des informations linguistiques. Journées d'Études sur la Parole, Jul 2016, Paris, France. ⟨hal-01321361⟩

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