Individual speech recognition in noise, the audiogram and more: Using automatic speech recognition (ASR) as a modelling tool

Authors

  • Birger Kollmeier Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany
  • Marc René Schädler Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany
  • Anna Warzybok Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany
  • Bernd T. Meyer Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany
  • Thomas Brand Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany

Abstract

To characterize the individual patient’s hearing impairment, a framework for auditory discrimination experiments (FADE, Schädler et al., 2015) was extended here using different degrees of individualization. FADE has been shown to predict the outcome of both speech recognition tests and psychoacoustic experiments based on simulations using an automatic speech recognition (ASR) system which  requires only few assumptions. It builds on the closed-set matrix sentence recognition test which is advantageous for testing individual speech recognition in a way comparable across languages. Individual predictions of speech recognition thresholds in stationary and in fluctuating noise were derived using the audiogram and an estimate of the internal detector noise (“level uncertainty”). Either “typical” audiogram shapes with or without a “typical” level uncertainty or the individual data were used for individual predictions. As a result, the individualisation of the level uncertainty was found to be more important than the exact shape of the individual audiogram to accurately model the outcome of the German matrix test in stationary or fluctuating noise for listeners with hearing impairment.

References

ANSI (1997). S3.5 Methods for Calculation of the Speech Intelligibility Index. Standards Secretariat, Acoustical Society of America.

Bisgaard, N., Vlaming, M.S., and Dahlquist, M. (2010) “Standard audiograms for the IEC 60118-15 measurement procedure,” Trends Amplif., 14, 113-120.

Brand,T. and Kollmeier, B. (2002). “Vorhersage der Sprachverständlichkeit in Ruhe und im Störgeräusch aufgrund des Reintonaudiogramms,” Proc. 5. Jahrestagung der Deutschen Gesellschaft für Audiologie, Zürich.

Dau, T., Kollmeier, B., and Kohlrausch, A. (1997). “Modeling auditory processing of amplitude modulation: I. Detection and masking with narrow band carrier,” J. Acoust. Soc. Am., 102, 2892-2905.

Holube, I. and Kollmeier, B. (1996). “Speech intelligibility prediction in hearing‐impaired listeners based on a psychoacoustically motivated perception model,” J. Acoust. Soc. Am., 100, 1703-1716.

Jürgens, T. and Brand, T. (2009). “Microscopic prediction of speech recognition for listeners with normal hearing in noise using an auditory model,” J. Acoust. Soc. Am., 126, 2635-2648.

Kollmeier, B., Warzybok, A., Hochmuth, S., Zokoll, M., Uslar, V.N., Brand, T., and Wagener, K.C. (2015). “The multilingual matrix test: principles, applications and comparison across languages - a review,” Int. J. Audiol. Suppl., 54, 3-16.

Meyer, R.M. and Brand, T. (2013). “Comparison of different short-term Speech Intelligibility Index procedures in fluctuating noise for listeners with normal and impaired hearing,” Acta Acust. United Ac., 99, 442-446.

Plomp, R. (1978). “Auditory handicap of hearing impairment and the limited benefit of hearing aids,” J. Acoust. Soc. Am., 63, 533-549.

Schädler, M., Warzybok, A., Hochmuth, S., and Kollmeier, B. (2015). “Matrix sentence intelligibility prediction using an automatic speech recognition system,” Int. J. Audiol. Suppl., 54, 100-107.

Schädler, M.R., Warzybok, A., Ewert, S.F., and Kollmeier, B. (2016). “A simulation framework for auditory discrimination experiments: Revealing the importance of across-frequency processing in speech perception,” Submitted.

Wagener, K.C., Brand, T., Kollmeier, B. (2006). “The role of silent intervals for sentence intelligibility in fluctuating noise in hearing-impaired listeners,” Int. J. Audiol., 45, 26-33.

Downloads

Published

2015-12-15

How to Cite

Kollmeier, B., Schädler, M. R., Warzybok, A., Meyer, B. T., & Brand, T. (2015). Individual speech recognition in noise, the audiogram and more: Using automatic speech recognition (ASR) as a modelling tool. Proceedings of the International Symposium on Auditory and Audiological Research, 5, 149–156. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2015-17

Issue

Vol. 5 (2015): Individual Hearing Loss - Characterization, modelling, compensation strategies

Section

2015/3. Modelling individual hearing impairment

License

Authors who publish with this journal agree to the following terms:

a. Authors retain copyright* and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

*From the 2017 issue onward. The Danavox Jubilee Foundation owns the copyright of all articles published in the 1969-2015 issues. However, authors are still allowed to share the work with an acknowledgement of the work's authorship and initial publication in this journal.