{"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/investigation-of-enhanced-tacotron-text-to","title":"Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language","arxiv_id":"1810.11960","date":"2018-10-29","proceeding":null,"authors":["Yusuke Yasuda","Xin Wang","Shinji Takaki","Junichi Yamagishi"],"abstract":"End-to-end speech synthesis is a promising approach that directly converts\nraw text to speech. Although it was shown that Tacotron2 outperforms classical\npipeline systems with regards to naturalness in English, its applicability to\nother languages is still unknown. Japanese could be one of the most difficult\nlanguages for which to achieve end-to-end speech synthesis, largely due to its\ncharacter diversity and pitch accents. Therefore, state-of-the-art systems are\nstill based on a traditional pipeline framework that requires a separate text\nanalyzer and duration model. Towards end-to-end Japanese speech synthesis, we\nextend Tacotron to systems with self-attention to capture long-term\ndependencies related to pitch accents and compare their audio quality with\nclassical pipeline systems under various conditions to show their pros and\ncons. In a large-scale listening test, we investigated the impacts of the\npresence of accentual-type labels, the use of force or predicted alignments,\nand acoustic features used as local condition parameters of the Wavenet\nvocoder. Our results reveal that although the proposed systems still do not\nmatch the quality of a top-line pipeline system for Japanese, we show important\nstepping stones towards end-to-end Japanese speech synthesis.","url_abs":"http://arxiv.org/abs/1810.11960v2","url_pdf":"http://arxiv.org/pdf/1810.11960v2.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":"abstracts"},"code_links":[{"paper_slug":"investigation-of-enhanced-tacotron-text-to","repo_url":"https://github.com/nii-yamagishilab/self-attention-tacotron","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"},{"task_slug":"text-to-speech","task_name":"Text to Speech"},{"task_slug":"text-to-speech-synthesis","task_name":"Text-To-Speech Synthesis"},{"task_slug":"text-to-speech-1","task_name":"text-to-speech"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bigru","method_name":"BiGRU"},{"method_slug":"cbhg","method_name":"CBHG"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gru","method_name":"GRU"},{"method_slug":"griffin-lim-algorithm","method_name":"Griffin-Lim Algorithm"},{"method_slug":"highway-layer","method_name":"Highway Layer"},{"method_slug":"highway-network","method_name":"Highway Network"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"residual-gru","method_name":"Residual GRU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tacotron","method_name":"Tacotron"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.11960","atlas_url":"https://app.syntology.ai/?focus=1810.11960","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}