Narrator or Character: Voice Modulation in an Expressive Multi-speaker TTS

Current Text-to-Speech (TTS) systems are trained on audiobook data and perform well in synthesizing read-style speech. In this work, we are interested in synthesizing audio stories as narrated to children. The storytelling style is more expressive and requires perceptible changes of voice across the narrator and story characters. To address these challenges, we present a new TTS corpus of English audio stories for children with 32.7 hours of speech by a single female speaker with a UK accent. We provide evidence of the salient differences in the suprasegmentals of the narrator and character utterances in the dataset, motivating the use of a multi-speaker TTS for our application. We use a fine-tuned BERT model to label each sentence as being spoken by a narrator or character that is subsequently used to condition the TTS output. Experiments show our new TTS system is superior in expressiveness in both A-B preference and MOS testing compared to reading-style TTS and single-speaker TTS.