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Computational Model Predictions of Cues for Concurrent Vowel Identification

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Abstract

Although differences in fundamental frequencies (F0s) between vowels are beneficial for their segregation and identification, listeners can still segregate and identify simultaneous vowels that have identical F0s, suggesting that additional cues are contributing, including formant frequency differences. The current perception and computational modeling study was designed to assess the contribution of F0 and formant difference cues for concurrent vowel identification. Younger adults with normal hearing listened to concurrent vowels over a wide range of levels (25–85 dB SPL) for conditions in which F0 was the same or different between vowel pairs. Vowel identification scores were poorer at the lowest and highest levels for each F0 condition, and F0 benefit was reduced at the lowest level as compared to higher levels. To understand the neural correlates underlying level-dependent changes in vowel identification, a computational auditory-nerve model was used to estimate formant and F0 difference cues under the same listening conditions. Template contrast and average localized synchronized rate predicted level-dependent changes in the strength of phase locking to F0s and formants of concurrent vowels, respectively. At lower levels, poorer F0 benefit may be attributed to poorer phase locking to both F0s, which resulted from lower firing rates of auditory-nerve fibers. At higher levels, poorer identification scores may relate to poorer phase locking to the second formant, due to synchrony capture by lower formants. These findings suggest that concurrent vowel identification may be partly influenced by level-dependent changes in phase locking of auditory-nerve fibers to F0s and formants of both vowels.

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ACKNOWLEDGMENTS

This work was supported (in part) by research grants R01 DC000184 and P50 DC000422 from NIH/NIDCD and by the South Carolina Clinical and Translational Research (SCTR) Institute, with an academic home at the Medical University of South Carolina, NIH/NCRR Grant number UL1 RR029882. This investigation was conducted in a facility constructed with support from Research Facilities Improvement Program Grant Number C06 RR14516 from the National Center for Research Resources, National Institutes of Health. We thank Ian C. Bruce and Emily Buss for sharing MATLAB code, Michael G. Heinz for his valuable suggestions, Tyler W. Eisenhart for data collection, and Skyler G. Jennings and William J. Bologna for providing valuable comments on a previous version of the manuscript.

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  1. Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, 135 Rutledge Avenue, MSC 550, Charleston, SC, 29425-5500, USA

    Ananthakrishna Chintanpalli, Jayne B. Ahlstrom & Judy R. Dubno

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  1. Ananthakrishna Chintanpalli
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  2. Jayne B. Ahlstrom
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  3. Judy R. Dubno
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Correspondence to Judy R. Dubno.

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Chintanpalli, A., Ahlstrom, J.B. & Dubno, J.R. Computational Model Predictions of Cues for Concurrent Vowel Identification. JARO 15, 823–837 (2014). https://doi.org/10.1007/s10162-014-0475-7

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  • DOI: https://doi.org/10.1007/s10162-014-0475-7

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