Literature DB >> 8817896

Speech intelligibility prediction in hearing-impaired listeners based on a psychoacoustically motivated perception model.

I Holube1, B Kollmeier.   

Abstract

Sensorneural hearing-impaired listeners suffer severely from deterioration in the processing and internal representation of acoustic signals. In order to understand this deterioration in detail, a numerical perception model was developed which is based on current functional models of the signal processing in the auditory system. To test this model, the individual's speech intelligibility in quiet and in noise was predicted. The primary input parameter of the model is the precisely measured audiogram of each listener. In a refined version of the model, additional input parameters are derived from predicting the individual's temporal forward masking and notched-noise measurements with the same model assumptions. The predictions of the perception model were compared with those of the articulation index (AI) and the speech transmission index (STI). The accuracy of prediction with the perception model is in the same range as with the AI and the STI. The model does not require a calibration function and has the advantage of a greater flexibility in including different processing deficits associated with hearing impairment. However, it requires more time for computation. For the hearing-impaired listeners examined so far the individually measured psychoacoustical parameters have only a secondary effect on the prediction as compared to the audiogram. Nevertheless, the underlying model is a first step toward a quantitative understanding of speech intelligibility and helps to distinguish between the influence of the "attenuation" and the "distortion" component of the hearing loss.

Entities:  

Mesh:

Year:  1996        PMID: 8817896     DOI: 10.1121/1.417354

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  12 in total

1.  Analysis of a simplified normalized covariance measure based on binary weighting functions for predicting the intelligibility of noise-suppressed speech.

Authors:  Fei Chen; Philipos C Loizou
Journal:  J Acoust Soc Am       Date:  2010-12       Impact factor: 1.840

2.  Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions.

Authors:  Jianfen Ma; Yi Hu; Philipos C Loizou
Journal:  J Acoust Soc Am       Date:  2009-05       Impact factor: 1.840

3.  Objective speech intelligibility measurement for cochlear implant users in complex listening environments.

Authors:  João F Santos; Stefano Cosentino; Oldooz Hazrati; Philipos C Loizou; Tiago H Falk
Journal:  Speech Commun       Date:  2013-09-01       Impact factor: 2.017

4.  Curriculum for graduate courses in amplification.

Authors:  C V Palmer
Journal:  Trends Amplif       Date:  1998-03

5.  Predicting the speech reception threshold of cochlear implant listeners using an envelope-correlation based measure.

Authors:  Nima Yousefian; Philipos C Loizou
Journal:  J Acoust Soc Am       Date:  2012-11       Impact factor: 1.840

6.  Objective Quality and Intelligibility Prediction for Users of Assistive Listening Devices.

Authors:  Tiago H Falk; Vijay Parsa; João F Santos; Kathryn Arehart; Oldooz Hazrati; Rainer Huber; James M Kates; Susan Scollie
Journal:  IEEE Signal Process Mag       Date:  2015-03       Impact factor: 12.551

7.  The contribution of obstruent consonants and acoustic landmarks to speech recognition in noise.

Authors:  Ning Li; Philipos C Loizou
Journal:  J Acoust Soc Am       Date:  2008-12       Impact factor: 1.840

8.  Sentence Recognition Prediction for Hearing-impaired Listeners in Stationary and Fluctuation Noise With FADE: Empowering the Attenuation and Distortion Concept by Plomp With a Quantitative Processing Model.

Authors:  Birger Kollmeier; Marc René Schädler; Anna Warzybok; Bernd T Meyer; Thomas Brand
Journal:  Trends Hear       Date:  2016-09-07       Impact factor: 3.293

9.  Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.

Authors:  Tobias Goehring; Federico Bolner; Jessica J M Monaghan; Bas van Dijk; Andrzej Zarowski; Stefan Bleeck
Journal:  Hear Res       Date:  2016-11-30       Impact factor: 3.208

10.  Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants.

Authors:  Tobias Goehring; Mahmoud Keshavarzi; Robert P Carlyon; Brian C J Moore
Journal:  J Acoust Soc Am       Date:  2019-07       Impact factor: 1.840

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