Year: 2023
Type: conference-paper
Abstract: This paper describes the GIPSA-Lab submission to the Blizzard Challenge 2023. The Text-To-Speech system trained for this challenge is a Transformer-based non-autoregressive encoderdecoder architecture based on FastSpeech2. Updates of the FastSpeech2 framework were provided to specifically train the model on orthographic inputs, which is our main focus for this edition of the challenge. This model was trained with both orthographic and phonetic transcriptions of the same dataset. An additional phonetic prediction layer was added to the model. This additional layer enables to train the text encoder on phonetic prediction alone, without the need for audio recordings.
Institution: GIPSA-Lab
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: MIAI @ Grenoble Alpes, G3010527862
OpenAlex ID: W4387941735