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Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Effect of temporal fine structure on speech intelligibility modeling.

Fei Chen, Tian Guan, Lena L N Wong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
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    Summary

    Temporal fine structure (TFS) is crucial for hearing-impaired speech perception. This study found that current intelligibility indices predict speech understanding better when using condition-specific mapping functions for TFS information.

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    Area of Science:

    • Audiology
    • Speech Processing
    • Hearing Science

    Background:

    • Temporal fine structure (TFS) is vital for speech perception, especially for individuals with hearing impairments.
    • Understanding TFS is critical for developing advanced prosthetic hearing devices.
    • Existing intelligibility indices need evaluation for their effectiveness with varying TFS information.

    Purpose of the Study:

    • To assess the performance of current speech intelligibility indices in predicting intelligibility for speech with different amounts of TFS information.
    • To determine the correlation between existing indices and actual intelligibility scores across various TFS conditions.
    • To explore methods for improving the accuracy of intelligibility prediction models.

    Main Methods:

    • Collected speech intelligibility data from Mandarin sentences processed to contain different levels of TFS information (vocoded for limited TFS, wideband for intact TFS).
    • Performed correlation analysis between collected intelligibility scores and predictions from existing intelligibility indices.
    • Investigated the impact of using general versus condition-dependent mapping functions to relate intelligibility measures to scores.

    Main Results:

    • Current intelligibility indices showed good performance when predicting intelligibility for vocoded or wideband speech independently.
    • A significant decrease in correlation was observed when a single, general function was used to map all intelligibility measures to scores.
    • Intelligibility prediction accuracy substantially improved when employing multiple, condition-dependent mapping functions.

    Conclusions:

    • Existing intelligibility indices require refinement to accurately predict speech intelligibility across diverse TFS conditions.
    • The use of condition-dependent mapping functions is essential for enhancing the predictive power of intelligibility indices for speech with varying TFS content.
    • Future prosthetic hearing device designs could benefit from intelligibility models that account for specific TFS characteristics.