Literature DB >> 20936997

Modelling the speech reception threshold in non-stationary noise in hearing-impaired listeners as a function of level.

Koenraad S Rhebergen1, Niek J Versfeld, Jan A P M de Laat, Wouter A Dreschler.   

Abstract

The extended speech intelligibility index (ESII) model (Rhebergen et al, 2006) forms an upgrade to the conventional speech intelligibility index model. For normal-hearing listeners the ESII model is able to predict the speech reception threshold (SRT) in both stationary and non-stationary noise maskers. In this paper, a first attempt is made to evaluate the ESII with SRT data obtained by de Laat and Plomp (1983), and Versfeld and Dreschler (2002) of hearing-impaired listeners in stationary, 10-Hz interrupted, and non-stationary speech-shaped noise measured at different noise levels. The results show that the ESII model is able to describe the SRT in different non-stationary noises for normal-hearing listeners at different noise levels reasonably well. However, the ESII model is less successful in the case of predicting the SRT in non-stationary noise for hearing-impaired subjects. As long as the present audibility models cannot describe the auditory processing in a listener with cochlear hearing loss accurately, it is difficult to distinguish between raised SRTs due to supra-threshold deficits or factors such as cognition, age, and language skills.

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Year:  2010        PMID: 20936997     DOI: 10.3109/14992027.2010.498446

Source DB:  PubMed          Journal:  Int J Audiol        ISSN: 1499-2027            Impact factor:   2.117


  4 in total

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Authors:  Nathaniel A Whitmal; Kristina DeRoy
Journal:  J Acoust Soc Am       Date:  2011-12       Impact factor: 1.840

2.  Abnormal intelligibility of speech in competing speech and in noise in a frequency region where audiometric thresholds are near-normal for hearing-impaired listeners.

Authors:  Agnès C Léger; David T Ives; Christian Lorenzi
Journal:  Hear Res       Date:  2014-08-11       Impact factor: 3.208

3.  Decreased Speech-In-Noise Understanding in Young Adults with Tinnitus.

Authors:  Annick Gilles; Winny Schlee; Sarah Rabau; Kristien Wouters; Erik Fransen; Paul Van de Heyning
Journal:  Front Neurosci       Date:  2016-06-28       Impact factor: 4.677

4.  Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms.

Authors:  Marc R Schädler; Anna Warzybok; Birger Kollmeier
Journal:  Trends Hear       Date:  2018 Jan-Dec       Impact factor: 3.293

  4 in total

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