Tao Wang , Jiangyan Yi , Ruibo Fu , Jianhua Tao , Zhengqi Wen , Chu Yuan Zhang
{"title":"Emotion selectable end-to-end text-based speech editing","authors":"Tao Wang , Jiangyan Yi , Ruibo Fu , Jianhua Tao , Zhengqi Wen , Chu Yuan Zhang","doi":"10.1016/j.artint.2024.104076","DOIUrl":null,"url":null,"abstract":"<div><p>Text-based speech editing is a convenient way for users to edit speech by intuitively cutting, copying, and pasting text. Previous work introduced CampNet, a context-aware mask prediction network that significantly improved the quality of edited speech. However, this paper proposes a new task: adding emotional effects to the edited speech during text-based speech editing to enhance the expressiveness and controllability of the edited speech. To achieve this, we introduce Emo-CampNet, which allows users to select emotional attributes for the generated speech and has the ability to edit the speech of unseen speakers. Firstly, the proposed end-to-end model controls the generated speech's emotion by introducing additional emotion attributes based on the context-aware mask prediction network. Secondly, to prevent emotional interference from the original speech, a neutral content generator is proposed to remove the emotional components, which is optimized using the generative adversarial framework. Thirdly, two data augmentation methods are proposed to enrich the emotional and pronunciation information in the training set. Experimental results<span><sup>1</sup></span> show that Emo-CampNet effectively controls the generated speech's emotion and can edit the speech of unseen speakers. Ablation experiments further validate the effectiveness of emotional selectivity and data augmentation methods.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0004370224000122","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Text-based speech editing is a convenient way for users to edit speech by intuitively cutting, copying, and pasting text. Previous work introduced CampNet, a context-aware mask prediction network that significantly improved the quality of edited speech. However, this paper proposes a new task: adding emotional effects to the edited speech during text-based speech editing to enhance the expressiveness and controllability of the edited speech. To achieve this, we introduce Emo-CampNet, which allows users to select emotional attributes for the generated speech and has the ability to edit the speech of unseen speakers. Firstly, the proposed end-to-end model controls the generated speech's emotion by introducing additional emotion attributes based on the context-aware mask prediction network. Secondly, to prevent emotional interference from the original speech, a neutral content generator is proposed to remove the emotional components, which is optimized using the generative adversarial framework. Thirdly, two data augmentation methods are proposed to enrich the emotional and pronunciation information in the training set. Experimental results1 show that Emo-CampNet effectively controls the generated speech's emotion and can edit the speech of unseen speakers. Ablation experiments further validate the effectiveness of emotional selectivity and data augmentation methods.
期刊介绍:
The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.