{"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/conversational-end-to-end-tts-for-voice-agent","title":"Conversational End-to-End TTS for Voice Agent","arxiv_id":"2005.10438","date":"2020-05-21","proceeding":null,"authors":["Haohan Guo","Shaofei Zhang","Frank K. Soong","Lei He","Lei Xie"],"abstract":"End-to-end neural TTS has achieved superior performance on reading style speech synthesis. However, it's still a challenge to build a high-quality conversational TTS due to the limitations of the corpus and modeling capability. This study aims at building a conversational TTS for a voice agent under sequence to sequence modeling framework. We firstly construct a spontaneous conversational speech corpus well designed for the voice agent with a new recording scheme ensuring both recording quality and conversational speaking style. Secondly, we propose a conversation context-aware end-to-end TTS approach which has an auxiliary encoder and a conversational context encoder to reinforce the information about the current utterance and its context in a conversation as well. Experimental results show that the proposed methods produce more natural prosody in accordance with the conversational context, with significant preference gains at both utterance-level and conversation-level. Moreover, we find that the model has the ability to express some spontaneous behaviors, like fillers and repeated words, which makes the conversational speaking style more realistic.","url_abs":"http://arxiv.org/abs/2005.10438v2","url_pdf":"http://arxiv.org/pdf/2005.10438v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"conversational-end-to-end-tts-for-voice-agent","repo_url":"https://github.com/keonlee9420/DailyTalk","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"conversational-end-to-end-tts-for-voice-agent","repo_url":"https://github.com/keonlee9420/Expressive-FastSpeech2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2005.10438","atlas_url":"https://app.syntology.ai/?focus=2005.10438","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}