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Hear Res. 2016 Sep;339:40-9. doi: 10.1016/j.heares.2016.06.001. Epub 2016 Jun 4.

Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A mismatch negativity study.

Author information

1
Department of Speech-Language-Hearing Sciences, University of Minnesota, Minneapolis, MN 55455, USA.
2
Department of Speech-Language-Hearing Sciences, University of Minnesota, Minneapolis, MN 55455, USA; Center for Neurobehavioral Development, University of Minnesota, Minneapolis, MN 55455, USA; Center for Applied Translational Sensory Science, University of Minnesota, Minneapolis, MN 55455, USA. Electronic address: zhanglab@umn.edu.
3
Department of Speech-Language-Hearing Sciences, University of Minnesota, Minneapolis, MN 55455, USA; Center for Applied Translational Sensory Science, University of Minnesota, Minneapolis, MN 55455, USA.
4
School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA.

Abstract

Successful speech communication requires the extraction of important acoustic cues from irrelevant background noise. In order to better understand this process, this study examined the effects of background noise on mismatch negativity (MMN) latency, amplitude, and spectral power measures as well as behavioral speech intelligibility tasks. Auditory event-related potentials (AERPs) were obtained from 15 normal-hearing participants to determine whether pre-attentive MMN measures recorded in response to a consonant (from /ba/ to /bu/) and vowel change (from /ba/ to /da/) in a double-oddball paradigm can predict sentence-level speech perception. The results showed that background noise increased MMN latencies and decreased MMN amplitudes with a reduction in the theta frequency band power. Differential noise-induced effects were observed for the pre-attentive processing of consonant and vowel changes due to different degrees of signal degradation by noise. Linear mixed-effects models further revealed significant correlations between the MMN measures and speech intelligibility scores across conditions and stimuli. These results confirm the utility of MMN as an objective neural marker for understanding noise-induced variations as well as individual differences in speech perception, which has important implications for potential clinical applications.

KEYWORDS:

Linear mixed effects model; MMN; Speech-in-noise perception; Theta band; Time-frequency analysis

PMID:
27267705
DOI:
10.1016/j.heares.2016.06.001
[Indexed for MEDLINE]

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