Multimodal Conditioning with Reference Speech and Text Embeddings for Natural and Expressive TTS on VCTK

Abstract

Abstract: This paper introduces a new end-to-end text-to-speech (E2E-TTS) toolkit named ESPnet-TTS, which is an extension of the open-source speech processing toolkit ESPnet. The toolkit supports state-of-the-art E2E-TTS models, including Tacotron2, Transformer TTS, and FastSpeech, and also provides recipes inspired by the Kaldi automatic speech recognition (ASR) toolkit. The recipes are based on the design unified with the ESPnet ASR recipe, providing high reproducibility. The toolkit also provides pre-trained models and samples of all of the recipes so that users can use it as a baseline. Furthermore

Research Question

What is the impact of using multimodal conditioning with reference speech and text embeddings on the naturalness and expressiveness of TTS models evaluated using the MOS metric on the VCTK benchmark?

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