Audiobook readers play with their voices to emphasize some text passages, highlight discourse changes or significant events, or in order to make listening easier and entertaining. A dialog is a central passage in audiobooks where the reader applies significant voice transformation, mainly prosodic modifications, to realize character properties and changes. However, these intra-speaker modifications are hard to reproduce with simple text-to-speech synthesis. The manner of vocalizing characters involved in a given story depends on the text style and differs from one speaker to another. In this work, this problem is investigated through the prism of voice conversion. We propose to explore modifying the narrator’s voice to fit the context of the story, such as the character who is speaking, using voice conversion. To this end, two complementary experiments are designed: the first one aims to assess the quality of our Phonetic PosteriorGrams (PPG)-based voice conversion system using parallel data. Subjective evaluations with naive raters are conducted to estimate the quality of the signal generated and the speaker similarity. The second experiment applies an intra-speaker voice conversion, considering narration passages and direct speech passages as two distinct speakers. Data are then nonparallel and the dissimilarity between character and narrator is subjectively measured.

Cet article présente une évaluation de modèles statistiques du langage menée sur la langue Française. Nous avons cherché à comparer la performance de modèles de langage exotiques par rapport aux modèles plus classiques de n-gramme à horizon fixe. Les expériences réalisées montrent que des modèles de n-gramme à horizon variable peuvent faire baisser de plus de 10% en moyenne la perplexité d’un modèle de n-gramme à horizon fixe. Les modèles de n/m-multigramme demandent une adaptation pour pouvoir être concurrentiels.