Abstract. Deep neural networks have become the state of the art in speech synthesis. They have been used to directly predict signal parameters or provide unsupervised speech segment descriptions through embeddings. In this paper, we present four models with two of them enabling us to extract phone-level embeddings for unit selection speech synthesis. Three of the models rely on a feed-forward DNN, the last one on an LSTM. The resulting embeddings enable replacing usual expert-based target costs by an euclidean distance in the embedding space. This work is conducted on a French corpus of an 11 hours audiobook. Perceptual tests show the produced speech is preferred over a unit selection method where the target cost is defined by an expert. They also show that the embeddings are general enough to be used for different speech styles without quality loss. Furthermore, objective measures and a perceptual test on statistical parametric speech synthesis show that our models perform comparably to state-of-the-art models for parametric signal generation, in spite of necessary simplifications, namely late time integration and information compression.

Breton is a minority language spoken in the Brittany region of France. Public initiatives are being undertaken in order to preserve the Breton language. As an effort toward that goal, we created a large Breton speech corpus and related automatic annotation tools. The corpus contains 20 hours of reading aloud for both a male and a female Breton speaker. Then, end-to-end text-to-speech synthesis systems are built. Subjective evaluation suggests that the systems are able to reproduce the voices of the original speakers faithfully. * David Guennec is now employed by ViaDialog * Hassan Hajipoor is now a PhD candidate at University of Massachusetts * Gwénolé Lecorvé is now employed by Orange Innovation

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