Olivier Perrotin , Brooke Stephenson , Silvain Gerber , Gérard Bailly , Simon King
{"title":"完善语音合成评估:2023 年暴风雪挑战赛总结","authors":"Olivier Perrotin , Brooke Stephenson , Silvain Gerber , Gérard Bailly , Simon King","doi":"10.1016/j.csl.2024.101747","DOIUrl":null,"url":null,"abstract":"<div><div>The Blizzard Challenge has benchmarked progress in Text-to-Speech (TTS) since 2005. The Challenge has seen important milestones passed, with results suggesting that synthetic speech was indistinguishable from natural speech in terms of intelligibility in 2021 and that by that same year it was perhaps even indistinguishable in naturalness. The high quality of synthetic speech generated by the latest TTS systems has thus revealed limitations with ITU-T P.800.1 Mean Opinion Score (MOS) in detecting the remaining differences between synthetic and natural speech. Yet, it was the only method used in previous Challenges and is still the most popular method in the field for speech synthesis evaluation. In the 2023 Challenge, we addressed observed limitations of past Challenges by incorporating state-of-the-art speech synthesis evaluation techniques to refine the evaluation of speech quality, speaker similarity and intelligibility. For speech quality, a relative comparison of the systems receiving the best MOS was able to discover a greater number of significant differences between systems. Regarding speaker similarity, we demonstrated that there is a strong bias depending on whether the listeners are familiar with the target voice or not. As for intelligibility, the evaluation of language-specific phenomena, such as the pronunciation of homographs, better highlighted system limits compared to global transcription tasks of synthesised utterances. In addition to reporting results for the 18 entries to the 2023 Challenge, we extend the results analysis to type of TTS module to provide some insights on the most recent advances in model design. Overall, this year’s results demonstrate the need for a shift towards new methods for refining TTS evaluation to shed light on increasingly smaller and localised differences between synthesised and natural speech.</div></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":"90 ","pages":"Article 101747"},"PeriodicalIF":3.1000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Refining the evaluation of speech synthesis: A summary of the Blizzard Challenge 2023\",\"authors\":\"Olivier Perrotin , Brooke Stephenson , Silvain Gerber , Gérard Bailly , Simon King\",\"doi\":\"10.1016/j.csl.2024.101747\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The Blizzard Challenge has benchmarked progress in Text-to-Speech (TTS) since 2005. The Challenge has seen important milestones passed, with results suggesting that synthetic speech was indistinguishable from natural speech in terms of intelligibility in 2021 and that by that same year it was perhaps even indistinguishable in naturalness. The high quality of synthetic speech generated by the latest TTS systems has thus revealed limitations with ITU-T P.800.1 Mean Opinion Score (MOS) in detecting the remaining differences between synthetic and natural speech. Yet, it was the only method used in previous Challenges and is still the most popular method in the field for speech synthesis evaluation. In the 2023 Challenge, we addressed observed limitations of past Challenges by incorporating state-of-the-art speech synthesis evaluation techniques to refine the evaluation of speech quality, speaker similarity and intelligibility. For speech quality, a relative comparison of the systems receiving the best MOS was able to discover a greater number of significant differences between systems. Regarding speaker similarity, we demonstrated that there is a strong bias depending on whether the listeners are familiar with the target voice or not. As for intelligibility, the evaluation of language-specific phenomena, such as the pronunciation of homographs, better highlighted system limits compared to global transcription tasks of synthesised utterances. In addition to reporting results for the 18 entries to the 2023 Challenge, we extend the results analysis to type of TTS module to provide some insights on the most recent advances in model design. Overall, this year’s results demonstrate the need for a shift towards new methods for refining TTS evaluation to shed light on increasingly smaller and localised differences between synthesised and natural speech.</div></div>\",\"PeriodicalId\":50638,\"journal\":{\"name\":\"Computer Speech and Language\",\"volume\":\"90 \",\"pages\":\"Article 101747\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Speech and Language\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S088523082400130X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S088523082400130X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Refining the evaluation of speech synthesis: A summary of the Blizzard Challenge 2023
The Blizzard Challenge has benchmarked progress in Text-to-Speech (TTS) since 2005. The Challenge has seen important milestones passed, with results suggesting that synthetic speech was indistinguishable from natural speech in terms of intelligibility in 2021 and that by that same year it was perhaps even indistinguishable in naturalness. The high quality of synthetic speech generated by the latest TTS systems has thus revealed limitations with ITU-T P.800.1 Mean Opinion Score (MOS) in detecting the remaining differences between synthetic and natural speech. Yet, it was the only method used in previous Challenges and is still the most popular method in the field for speech synthesis evaluation. In the 2023 Challenge, we addressed observed limitations of past Challenges by incorporating state-of-the-art speech synthesis evaluation techniques to refine the evaluation of speech quality, speaker similarity and intelligibility. For speech quality, a relative comparison of the systems receiving the best MOS was able to discover a greater number of significant differences between systems. Regarding speaker similarity, we demonstrated that there is a strong bias depending on whether the listeners are familiar with the target voice or not. As for intelligibility, the evaluation of language-specific phenomena, such as the pronunciation of homographs, better highlighted system limits compared to global transcription tasks of synthesised utterances. In addition to reporting results for the 18 entries to the 2023 Challenge, we extend the results analysis to type of TTS module to provide some insights on the most recent advances in model design. Overall, this year’s results demonstrate the need for a shift towards new methods for refining TTS evaluation to shed light on increasingly smaller and localised differences between synthesised and natural speech.
期刊介绍:
Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language.
The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.