2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPE-34.1 | ||
| Paper Title | LOW-RESOURCE EXPRESSIVE TEXT-TO-SPEECH USING DATA AUGMENTATION | ||
| Authors | Goeric Huybrechts, Thomas Merritt, Giulia Comini, Bartek Perz, Raahil Shah, Jaime Lorenzo-Trueba, Amazon, United Kingdom | ||
| Session | SPE-34: Speech Synthesis 6: Data Augmentation & Adaptation | ||
| Location | Gather.Town | ||
| Session Time: | Thursday, 10 June, 13:00 - 13:45 | ||
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-SYNT] Speech Synthesis and Generation | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style. In this work, we present a novel 3-step methodology to circumvent the costly operation of recording large amounts of target data in order to build expressive style voices with as little as 15 minutes of such recordings. First, we augment data via voice conversion by leveraging recordings in the desired speaking style from other speakers. Next, we use that synthetic data on top of the available recordings to train a TTS model. Finally, we fine-tune that model to further increase quality. Our evaluations show that the proposed changes bring significant improvements over non-augmented models across many perceived aspects of synthesised speech. We demonstrate the proposed approach on 2 styles (newscaster and conversational), on various speakers, and on both single and multi-speaker models, illustrating the robustness of our approach. | ||