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      ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit

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          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 Tacotron~2, 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, the unified design enables the integration of ASR functions with TTS, e.g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models. This paper describes the design of the toolkit and experimental evaluation in comparison with other toolkits. The experimental results show that our best model outperforms other toolkits, resulting in a mean opinion score (MOS) of 4.25 on the LJSpeech dataset. The toolkit is available on GitHub.

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          Speaker-Dependent WaveNet Vocoder

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            Speech Synthesis Based on Hidden Markov Models

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              ESPnet: End-to-End Speech Processing Toolkit

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                Author and article information

                Journal
                24 October 2019
                Article
                1910.10909

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Submitted to ICASSP2020. Demo HP: https://espnet.github.io/icassp2020-tts/
                cs.CL eess.AS

                Theoretical computer science, Electrical engineering

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