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Objective Quality and Intelligibility Prediction for Users of Assistive Listening Devices
Tiago H Falk1, Vijay Parsa2, João F Santos1
1INRS-EMT, University of Quebec, Montreal, QC, Canada.
IEEE Signal Processing Magazine
|June 9, 2015
Summary
This study reviews twelve objective speech quality and intelligibility prediction tools, including those for hearing-impaired listeners. It highlights their pros and cons for various conditions, aiding tool selection.
Area of Science:
- Acoustics
- Signal Processing
- Audiology
Background:
- Objective speech quality and intelligibility prediction are crucial for evaluating audio processing algorithms.
- Existing tools vary in methodology, requiring careful selection based on application.
- Tools for hearing-impaired (HI) listeners using assistive devices are increasingly important.
Purpose of the Study:
- To provide a comprehensive overview of twelve objective speech quality and intelligibility prediction tools.
- To classify algorithms into intrusive and non-intrusive methods.
- To evaluate tools for both normal-hearing and hearing-impaired listeners.
Main Methods:
- Reviewed intrusive and non-intrusive objective prediction algorithms.
- Included metrics for normal-hearing and hearing-impaired (HI) listeners (e.g., SRMR-HA, SRMR-CI, ModA, HASQI, HASPI, PEMO-Q-HI).
- Tested metrics on subjective speech datasets with various degradations (reverberation, noise, nonlinear frequency compression, speech enhancement).
Main Results:
- Detailed analysis of the advantages and limitations of each objective metric.
- Performance evaluation across different degradation types and assistive listening device conditions.
- Comparison of metrics tailored for hearing aids (HAs) and cochlear implants (CIs).
Conclusions:
- No single objective tool is universally optimal for all conditions.
- Recommendations provided for selecting appropriate tools based on specific environmental and processing scenarios.
- Guidance for optimizing speech quality and intelligibility assessment for diverse listener groups.
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