{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T02:03:55Z","timestamp":1791770635487,"version":"4.3.4"},"reference-count":54,"publisher":"Acoustical Society of America (ASA)","issue":"3","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cluster of Excellence Hearing4all","award":["1077\/1"],"award-info":[{"award-number":["1077\/1"]}]}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,3,1]]},"abstract":"<jats:p>Automatic speech recognition (ASR) has made major progress based on deep machine learning, which motivated the use of deep neural networks (DNNs) as perception models and specifically to predict human speech recognition (HSR). This study investigates if a modeling approach based on a DNN that serves as phoneme classifier [Spille, Ewert, Kollmeier, and Meyer (2018). Comput. Speech Lang. 48, 51\u201366] can predict HSR for subjects with different degrees of hearing loss when listening to speech embedded in different complex noises. The eight noise signals range from simple stationary noise to a single competing talker and are added to matrix sentences, which are presented to 20 hearing-impaired (HI) listeners (categorized into three groups with different types of age-related hearing loss) to measure their speech recognition threshold (SRT), i.e., the signal-to-noise ratio with 50% word recognition rate. These are compared to responses obtained from the ASR-based model using degraded feature representations that take into account the individual hearing loss of the participants captured by a pure-tone audiogram. Additionally, SRTs obtained from eight normal-hearing (NH) listeners are analyzed. For NH subjects and three groups of HI listeners, the average SRT prediction error is below 2\u2009dB, which is lower than the errors of the baseline models.<\/jats:p>","DOI":"10.1121\/10.0009411","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T16:45:48Z","timestamp":1646153148000},"page":"1417-1427","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":22,"title":["A model of speech recognition for hearing-impaired listeners based on deep learning"],"prefix":"10.1121","volume":"151","author":[{"given":"Jana","family":"Ro\u00dfbach","sequence":"first","affiliation":[{"name":"Communication Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky University 1 , D-26111 Oldenburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Birger","family":"Kollmeier","sequence":"additional","affiliation":[{"name":"Medical Physics and Cluster of Excellence Hearing4all, Carl von Ossietzky University 2 , D-26111 Oldenburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernd T.","family":"Meyer","sequence":"additional","affiliation":[{"name":"Communication Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky University 1 , D-26111 Oldenburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"231","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"2023081002564870900_c1","doi-asserted-by":"publisher","first-page":"5085","DOI":"10.1109\/ICASSP.2017.7953125","article-title":"A non-intrusive Short-Time Objective Intelligibility measure","year":"2017"},{"key":"2023081002564870900_c2","volume-title":"Methods for the Calculation of the Articulation Index","author":"ANSI","year":"1969"},{"key":"2023081002564870900_c3","volume-title":"American National Standard Methods for Calculation of the Speech Intelligibility Index","author":"ANSI","year":"1997"},{"issue":"1","key":"2023081002564870900_c4","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1121\/1.2202888","article-title":"Prediction of speech intelligibility in spatial noise and reverberation for normal-hearing and hearing-impaired listeners","volume":"120","year":"2006","journal-title":"J. Acoust. Soc. Am."},{"issue":"2","key":"2023081002564870900_c5","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1121\/1.4960574","article-title":"Envelope and intensity based prediction of psychoacoustic masking and speech intelligibility","volume":"140","year":"2016","journal-title":"J. Acoust. Soc. Am."},{"issue":"2","key":"2023081002564870900_c6","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1177\/1084713810379609","article-title":"Standard audiograms for the IEC 60118-15 measurement procedure","volume":"14","year":"2010","journal-title":"Trends Amplif."},{"issue":"6","key":"2023081002564870900_c7","doi-asserted-by":"publisher","first-page":"2801","DOI":"10.1121\/1.1479152","article-title":"Efficient adaptive procedures for threshold and concurrent slope estimates for psychophysics and speech intelligibility tests","volume":"111","year":"2002","journal-title":"J. Acoust. Soc. Am."},{"issue":"1","key":"2023081002564870900_c8","first-page":"117","article-title":"The cocktail party phenomenon: A review of research on speech intelligibility in multiple-talker conditions","volume":"86","year":"2000","journal-title":"Acustica"},{"key":"2023081002564870900_c9","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.specom.2018.11.006","article-title":"DNN-based performance measures for predicting error rates in automatic speech recognition and optimizing hearing aid parameters","volume":"106","year":"2019","journal-title":"Speech Commun."},{"issue":"3","key":"2023081002564870900_c10","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1121\/1.2166600","article-title":"A glimpsing model of speech perception in noise","volume":"119","year":"2006","journal-title":"J. Acoust. Soc. Am."},{"issue":"5","key":"2023081002564870900_c11","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.1121\/1.420344","article-title":"Modeling auditory processing of amplitude modulation. I. Detection and masking with narrow-band carriers","volume":"102","year":"1997","journal-title":"J. Acoust. Soc. Am."},{"issue":"3","key":"2023081002564870900_c12","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1121\/1.1288665","article-title":"Characterizing frequency selectivity for envelope fluctuations","volume":"108","year":"2000","journal-title":"J. Acoust. Soc. Am."},{"key":"2023081002564870900_c13","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1103\/RevModPhys.12.47","article-title":"Auditory patterns","volume":"12","year":"1940","journal-title":"Rev. Mod. Phys."},{"key":"2023081002564870900_c14","doi-asserted-by":"publisher","first-page":"233121652091476","DOI":"10.1177\/2331216520914769","article-title":"Predicting speech perception in older listeners with sensorineural hearing loss using automatic speech recognition","volume":"24","year":"2020","journal-title":"Trends Hear."},{"key":"2023081002564870900_c15","unstructured":"Fraunhofer IDMT (2014). \u201cSIP Toolbox,\u201d https:\/\/www.idmt.fraunhofer.de\/de\/institute\/projects-products\/sip-toolbox.html (Last viewed January 11, 2021)."},{"key":"2023081002564870900_c16","first-page":"S24","article-title":"Insights into spoken language gleaned from phonetic transcription of the switchboard corpus","year":"1996"},{"issue":"12","key":"2023081002564870900_c17","doi-asserted-by":"publisher","first-page":"891","DOI":"10.3109\/14992027.2010.506889","article-title":"Development and analysis of an international speech test signal (ISTS)","volume":"49","year":"2010","journal-title":"Int. J. Audiol."},{"issue":"3","key":"2023081002564870900_c18","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1121\/1.417354","article-title":"Speech intelligibility prediction in hearing-impaired listeners based on a psychoacoustically motivated perception model","volume":"100","year":"1996","journal-title":"J. Acoust. Soc. Am."},{"key":"2023081002564870900_c19","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.heares.2017.12.014","article-title":"Single-ended prediction of listening effort using deep neural networks","volume":"359","year":"2018","journal-title":"Hear. Res."},{"issue":"10","key":"2023081002564870900_c20","doi-asserted-by":"publisher","first-page":"759","DOI":"10.17743\/jaes.2018.0041","article-title":"Single-ended speech quality prediction based on automatic speech recognition","volume":"66","year":"2018","journal-title":"AES: J. Audio Eng. Soc."},{"key":"2023081002564870900_c21","first-page":"86","article-title":"Objective assessment of a speech enhancement scheme with an automatic speech recognition-based system","year":"2018"},{"issue":"11","key":"2023081002564870900_c22","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1109\/TASLP.2016.2585878","article-title":"An algorithm for predicting the intelligibility of speech masked by modulated noise maskers","volume":"24","year":"2016","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Proc."},{"issue":"3","key":"2023081002564870900_c23","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1121\/1.3621502","article-title":"Predicting speech intelligibility based on the signal-to-noise envelope power ratio after modulation-frequency selective processing","volume":"130","year":"2011","journal-title":"J. Acoust. Soc. Am."},{"issue":"1","key":"2023081002564870900_c24","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1121\/1.4807563","article-title":"A multi-resolution envelope-power based model for speech intelligibility","volume":"134","year":"2013","journal-title":"J. Acoust. Soc. Am."},{"issue":"5","key":"2023081002564870900_c25","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.1121\/1.3224721","article-title":"Microscopic prediction of speech recognition for listeners with normal hearing in noise using an auditory model","volume":"126","year":"2009","journal-title":"J. Acoust. Soc. Am."},{"key":"2023081002564870900_c26","unstructured":"Karbasi, M., Bleeck, S., and Kolossa, D. (2020). \u201cNon-intrusive speech intelligibility prediction using automatic speech recognition derived measures,\u201d http:\/\/arxiv.org\/abs\/2010.08574 (Last viewed May 14, 2021)."},{"key":"2023081002564870900_c27","doi-asserted-by":"publisher","first-page":"050184","DOI":"10.1121\/1.4799223","article-title":"An auditory model for intelligibility and quality predictions","volume":"19","year":"2013","journal-title":"Proc. Mtgs. Acoust."},{"issue":"4","key":"2023081002564870900_c28","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1121\/1.1862575","article-title":"Coherence and the speech intelligibility index","volume":"117","year":"2005","journal-title":"J. Acoust. Soc. Am."},{"key":"2023081002564870900_c29","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.specom.2014.06.002","article-title":"The hearing-aid speech perception index (HASPI)","volume":"65","year":"2014","journal-title":"Speech Commun."},{"issue":"S3","key":"2023081002564870900_c30","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1080\/14992027.2016.1256504","article-title":"Functionality of hearing aids: State-of-the-art and future model-based solutions","volume":"57","year":"2018","journal-title":"Int. J. Audiol."},{"issue":"5","key":"2023081002564870900_c31","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1121\/1.3238248","article-title":"A simple single-interval adaptive procedure for estimating thresholds in normal and impaired listeners","volume":"126","year":"2009","journal-title":"J. Acoust. Soc. Am."},{"key":"2023081002564870900_c32","first-page":"2982","article-title":"Autonomous measurement of speech intelligibility utilizing automatic speech recognition","year":"2015"},{"issue":"3","key":"2023081002564870900_c33","doi-asserted-by":"publisher","first-page":"442","DOI":"10.3813\/AAA.918625","article-title":"Comparison of different short-term speech intelligibility index procedures in fluctuating noise for listeners with normal and impaired hearing","volume":"99","year":"2013","journal-title":"Acta Acust. Acust."},{"issue":"11","key":"2023081002564870900_c34","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.1109\/TASLP.2015.2456420","article-title":"An auditory inspired amplitude modulation filter bank for robust feature extraction in automatic speech recognition","volume":"23","year":"2015","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2023081002564870900_c35","first-page":"468","article-title":"A CHiME-3 challenge system: Long-term acoustic features for noise robust automatic speech recognition","year":"2015"},{"issue":"12","key":"2023081002564870900_c36","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TASLP.2016.2615239","article-title":"Integration of optimized modulation filter sets into deep neural networks for automatic speech recognition","volume":"24","year":"2016","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Proc."},{"key":"2023081002564870900_c37","first-page":"123","volume-title":"Auditory Filters and Excitation Patterns as Representations of Frequency Resolution","year":"1986"},{"issue":"2","key":"2023081002564870900_c38","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1121\/1.381753","article-title":"Auditory handicap of hearing impairment and the limited benefit of hearing aids","volume":"63","year":"1978","journal-title":"J. Acoust. Soc. Am."},{"key":"2023081002564870900_c39","first-page":"1","article-title":"The Kaldi speech recognition toolkit","year":"2011"},{"issue":"4","key":"2023081002564870900_c40","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1121\/1.1861713","article-title":"A Speech Intelligibility Index-based approach to predict the speech reception threshold for sentences in fluctuating noise for normal-hearing listeners","volume":"117","year":"2005","journal-title":"J. Acoust. Soc. Am."},{"issue":"6","key":"2023081002564870900_c41","doi-asserted-by":"publisher","first-page":"3988","DOI":"10.1121\/1.2358008","article-title":"Extended speech intelligibility index for the prediction of the speech reception threshold in fluctuating noise","volume":"120","year":"2006","journal-title":"J. Acoust. Soc. Am."},{"issue":"1","key":"2023081002564870900_c42","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","year":"1978","journal-title":"IEEE Trans. Acoust. Speech Sign. Process."},{"key":"2023081002564870900_c43","doi-asserted-by":"publisher","first-page":"2331216520938929","DOI":"10.1177\/2331216520938929","article-title":"Individual aided speech-recognition performance and predictions of benefit for listeners with impaired hearing employing FADE","volume":"24","year":"2020","journal-title":"Trends Hear."},{"issue":"5","key":"2023081002564870900_c44","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1121\/1.4948772","article-title":"A simulation framework for auditory discrimination experiments: Revealing the importance of across-frequency processing in speech perception","volume":"139","year":"2016","journal-title":"J. Acoust. Soc. Am."},{"key":"2023081002564870900_c45","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3109\/14992027.2015.1061708","article-title":"Matrix sentence intelligibility prediction using an automatic speech recognition system","volume":"54","year":"2015","journal-title":"Int. J. Audiology"},{"key":"2023081002564870900_c46","doi-asserted-by":"publisher","first-page":"233121651876895","DOI":"10.1177\/2331216518768954","article-title":"Objective prediction of hearing aid benefit across listener groups using machine learning: Speech recognition performance with binaural noise-reduction algorithms","volume":"22","year":"2018","journal-title":"Trends Hear."},{"issue":"1","key":"2023081002564870900_c47","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1121\/1.4955079","article-title":"Monaural speech intelligibility and detection in maskers with varying amounts of spectro-temporal speech features","volume":"140","year":"2016","journal-title":"J. Acoust. Soc. Am."},{"issue":"8","key":"2023081002564870900_c48","doi-asserted-by":"publisher","first-page":"e73590","DOI":"10.1371\/journal.pone.0073590","article-title":"Tuning of human modulation filters is carrier-frequency dependent","volume":"8","year":"2013","journal-title":"PLoS One"},{"key":"2023081002564870900_c49","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.csl.2017.10.004","article-title":"Predicting speech intelligibility with deep neural networks","volume":"48","year":"2018","journal-title":"Comput. Speech Lang."},{"issue":"7","key":"2023081002564870900_c50","doi-asserted-by":"publisher","first-page":"2125","DOI":"10.1109\/TASL.2011.2114881","article-title":"An algorithm for intelligibility prediction of time-frequency weighted noisy speech","volume":"19","year":"2011","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"2023081002564870900_c51","first-page":"2345","article-title":"Sequence-discriminative Training of Deep Neural Networks","year":"2013"},{"issue":"3","key":"2023081002564870900_c52","first-page":"86","article-title":"Development and evaluation of a German sentence test Part III: Evaluation of the Oldenburg sentence test","volume":"38","year":"1999","journal-title":"Z. Audiol."},{"issue":"2","key":"2023081002564870900_c53","first-page":"44","article-title":"Entwicklung und Evaluation eines Satztests f\u00fcr die deutsche Sprache Teil II: Optimierung des Oldenburger Satztests","volume":"38","year":"1999","journal-title":"Z. Audiol."},{"issue":"1","key":"2023081002564870900_c54","first-page":"4","article-title":"Entwicklung und Evaluation eines Satztests f\u00fcr die deutsche Sprache I: Design des Oldenburger Satztests","volume":"38","year":"1999","journal-title":"Z. Audiol."}],"container-title":["The Journal of the Acoustical Society of America"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.aip.org\/asa\/jasa\/article-pdf\/151\/3\/1417\/16522425\/1417_1_online.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/pubs.aip.org\/asa\/jasa\/article-pdf\/151\/3\/1417\/16522425\/1417_1_online.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.aip.org\/asa\/jasa\/article-pdf\/151\/3\/1417\/16522425\/1417_1_online.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T19:14:38Z","timestamp":1748373278000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.aip.org\/jasa\/article\/151\/3\/1417\/2838087\/A-model-of-speech-recognition-for-hearing-impaired"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3,1]]}},"URL":"https:\/\/doi.org\/10.1121\/10.0009411","relation":{},"ISSN":["0001-4966","1520-8524"],"issn-type":[{"value":"0001-4966","type":"print"},{"value":"1520-8524","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,3]]},"published":{"date-parts":[[2022,3,1]]}}}