Evaluation of Speaker-Conditioned Target Speaker Extraction Algorithms for Hearing-Impaired Listeners.

IF 3 2区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Trends in Hearing Pub Date : 2025-01-01 Epub Date: 2025-08-11 DOI:10.1177/23312165251365802
Ragini Sinha, Ann-Christin Scherer, Simon Doclo, Christian Rollwage, Jan Rennies
{"title":"Evaluation of Speaker-Conditioned Target Speaker Extraction Algorithms for Hearing-Impaired Listeners.","authors":"Ragini Sinha, Ann-Christin Scherer, Simon Doclo, Christian Rollwage, Jan Rennies","doi":"10.1177/23312165251365802","DOIUrl":null,"url":null,"abstract":"<p><p>Speaker-conditioned target speaker extraction algorithms aim at extracting the target speaker from a mixture of multiple speakers by using additional information about the target speaker. Previous studies have evaluated the performance of these algorithms using either instrumental measures or subjective assessments with normal-hearing listeners or with hearing-impaired listeners. Notably, a previous study employing a quasicausal algorithm reported significant intelligibility improvements for both normal-hearing and hearing-impaired listeners, while another study demonstrated that a fully causal algorithm could enhance speech intelligibility and reduce listening effort for normal-hearing listeners. Building on these findings, this study focuses on an in-depth subjective assessment of two fully causal deep neural network-based speaker-conditioned target speaker extraction algorithms with hearing-impaired listeners, both without hearing loss compensation (unaided) and with linear hearing loss compensation (aided). Three different subjective performance measurement methods were used to cover a broad range of listening conditions, namely paired comparison, speech recognition thresholds, and categorically scaled perceived listening effort. The subjective evaluation results with 15 hearing-impaired listeners showed that one algorithm significantly reduced listening effort and improved intelligibility compared to unprocessed stimuli and the other algorithm. The data also suggest that hearing-impaired listeners experience a greater benefit in terms of listening effort (for both male and female interfering speakers) and speech recognition thresholds, especially in the presence of female interfering speakers than normal-hearing listeners, and that hearing loss compensation (linear amplification) is not required to obtain an algorithm benefit.</p>","PeriodicalId":48678,"journal":{"name":"Trends in Hearing","volume":"29 ","pages":"23312165251365802"},"PeriodicalIF":3.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12340209/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Hearing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/23312165251365802","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Speaker-conditioned target speaker extraction algorithms aim at extracting the target speaker from a mixture of multiple speakers by using additional information about the target speaker. Previous studies have evaluated the performance of these algorithms using either instrumental measures or subjective assessments with normal-hearing listeners or with hearing-impaired listeners. Notably, a previous study employing a quasicausal algorithm reported significant intelligibility improvements for both normal-hearing and hearing-impaired listeners, while another study demonstrated that a fully causal algorithm could enhance speech intelligibility and reduce listening effort for normal-hearing listeners. Building on these findings, this study focuses on an in-depth subjective assessment of two fully causal deep neural network-based speaker-conditioned target speaker extraction algorithms with hearing-impaired listeners, both without hearing loss compensation (unaided) and with linear hearing loss compensation (aided). Three different subjective performance measurement methods were used to cover a broad range of listening conditions, namely paired comparison, speech recognition thresholds, and categorically scaled perceived listening effort. The subjective evaluation results with 15 hearing-impaired listeners showed that one algorithm significantly reduced listening effort and improved intelligibility compared to unprocessed stimuli and the other algorithm. The data also suggest that hearing-impaired listeners experience a greater benefit in terms of listening effort (for both male and female interfering speakers) and speech recognition thresholds, especially in the presence of female interfering speakers than normal-hearing listeners, and that hearing loss compensation (linear amplification) is not required to obtain an algorithm benefit.

针对听障听众的说话人条件目标说话人提取算法评价。
基于说话人条件的目标说话人提取算法旨在利用目标说话人的附加信息从混合的多个说话人中提取目标说话人。以前的研究使用仪器测量或主观评估对听力正常或听力受损的听众评估这些算法的性能。值得注意的是,先前一项采用准因果算法的研究报告了正常听力和听力受损听众的可理解性显著提高,而另一项研究表明,完全因果算法可以提高正常听力听众的语音可理解性,减少听力努力。在这些发现的基础上,本研究重点对两种完全因果的基于深度神经网络的说话人条件目标说话人提取算法进行了深入的主观评估,这两种算法都是针对听力受损的听众,没有听力损失补偿(无辅助)和线性听力损失补偿(辅助)。我们使用了三种不同的主观表现测量方法来涵盖广泛的听力条件,即配对比较、语音识别阈值和分类缩放的感知听力努力。对15名听障听众的主观评价结果表明,与未处理的刺激和另一种算法相比,一种算法显著减少了听力努力,提高了可理解性。数据还表明,听力受损的听众在听力努力(男性和女性干扰扬声器)和语音识别阈值方面比正常听力的听众获得更大的好处,特别是在女性干扰扬声器存在的情况下,并且不需要听力损失补偿(线性放大)来获得算法优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Trends in Hearing
Trends in Hearing AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGYOTORH-OTORHINOLARYNGOLOGY
CiteScore
4.50
自引率
11.10%
发文量
44
审稿时长
12 weeks
期刊介绍: Trends in Hearing is an open access journal completely dedicated to publishing original research and reviews focusing on human hearing, hearing loss, hearing aids, auditory implants, and aural rehabilitation. Under its former name, Trends in Amplification, the journal established itself as a forum for concise explorations of all areas of translational hearing research by leaders in the field. Trends in Hearing has now expanded its focus to include original research articles, with the goal of becoming the premier venue for research related to human hearing and hearing loss.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信