Literature DB >> 28793162

Automatic Speech Recognition Predicts Speech Intelligibility and Comprehension for Listeners With Simulated Age-Related Hearing Loss.

Lionel Fontan1,2, Isabelle Ferrané2, Jérôme Farinas2, Julien Pinquier2, Julien Tardieu3, Cynthia Magnen3, Pascal Gaillard4, Xavier Aumont1, Christian Füllgrabe5.   

Abstract

Purpose: The purpose of this article is to assess speech processing for listeners with simulated age-related hearing loss (ARHL) and to investigate whether the observed performance can be replicated using an automatic speech recognition (ASR) system. The long-term goal of this research is to develop a system that will assist audiologists/hearing-aid dispensers in the fine-tuning of hearing aids. Method: Sixty young participants with normal hearing listened to speech materials mimicking the perceptual consequences of ARHL at different levels of severity. Two intelligibility tests (repetition of words and sentences) and 1 comprehension test (responding to oral commands by moving virtual objects) were administered. Several language models were developed and used by the ASR system in order to fit human performances.
Results: Strong significant positive correlations were observed between human and ASR scores, with coefficients up to .99. However, the spectral smearing used to simulate losses in frequency selectivity caused larger declines in ASR performance than in human performance.
Conclusion: Both intelligibility and comprehension scores for listeners with simulated ARHL are highly correlated with the performances of an ASR-based system. In the future, it needs to be determined if the ASR system is similarly successful in predicting speech processing in noise and by older people with ARHL.

Entities:  

Mesh:

Year:  2017        PMID: 28793162     DOI: 10.1044/2017_JSLHR-S-16-0269

Source DB:  PubMed          Journal:  J Speech Lang Hear Res        ISSN: 1092-4388            Impact factor:   2.297


  3 in total

1.  Noise, Age, and Gender Effects on Speech Intelligibility and Sentence Comprehension for 11- to 13-Year-Old Children in Real Classrooms.

Authors:  Nicola Prodi; Chiara Visentin; Erika Borella; Irene C Mammarella; Alberto Di Domenico
Journal:  Front Psychol       Date:  2019-09-25

2.  Deep Neural Network Model of Hearing-Impaired Speech-in-Noise Perception.

Authors:  Stephanie Haro; Christopher J Smalt; Gregory A Ciccarelli; Thomas F Quatieri
Journal:  Front Neurosci       Date:  2020-12-15       Impact factor: 4.677

3.  Predicting Speech Perception in Older Listeners with Sensorineural Hearing Loss Using Automatic Speech Recognition.

Authors:  Lionel Fontan; Tom Cretin-Maitenaz; Christian Füllgrabe
Journal:  Trends Hear       Date:  2020 Jan-Dec       Impact factor: 3.293

  3 in total

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