{"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/171105447","title":"Emotional End-to-End Neural Speech Synthesizer","arxiv_id":"1711.05447","date":"2017-11-15","proceeding":null,"authors":["Young-Gun Lee","Azam Rabiee","Soo-Young Lee"],"abstract":"In this paper, we introduce an emotional speech synthesizer based on the\nrecent end-to-end neural model, named Tacotron. Despite its benefits, we found\nthat the original Tacotron suffers from the exposure bias problem and\nirregularity of the attention alignment. Later, we address the problem by\nutilization of context vector and residual connection at recurrent neural\nnetworks (RNNs). Our experiments showed that the model could successfully train\nand generate speech for given emotion labels.","url_abs":"http://arxiv.org/abs/1711.05447v2","url_pdf":"http://arxiv.org/pdf/1711.05447v2.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":"171105447","repo_url":"https://github.com/AzamRabiee/Emotional-TTS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"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":{"atlas_url":"https://app.syntology.ai/?focus=1711.05447","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}