{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tacotron-towards-end-to-end-speech-synthesis","title":"Tacotron: Towards End-to-End Speech Synthesis","arxiv_id":"1703.10135","date":"2017-03-29","proceeding":null,"authors":["Yuxuan Wang","RJ Skerry-Ryan","Daisy Stanton","Yonghui Wu","Ron J. Weiss","Navdeep Jaitly","Zongheng Yang","Ying Xiao","Zhifeng Chen","Samy Bengio","Quoc Le","Yannis Agiomyrgiannakis","Rob Clark","Rif A. Saurous"],"abstract":"A text-to-speech synthesis system typically consists of multiple stages, such\nas a text analysis frontend, an acoustic model and an audio synthesis module.\nBuilding these components often requires extensive domain expertise and may\ncontain brittle design choices. In this paper, we present Tacotron, an\nend-to-end generative text-to-speech model that synthesizes speech directly\nfrom characters. Given <text, audio> pairs, the model can be trained completely\nfrom scratch with random initialization. We present several key techniques to\nmake the sequence-to-sequence framework perform well for this challenging task.\nTacotron achieves a 3.82 subjective 5-scale mean opinion score on US English,\noutperforming a production parametric system in terms of naturalness. In\naddition, since Tacotron generates speech at the frame level, it's\nsubstantially faster than sample-level autoregressive methods.","url_abs":"http://arxiv.org/abs/1703.10135v2","url_pdf":"http://arxiv.org/pdf/1703.10135v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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