A data-driven speech intelligibility assessment method using sum-sorted spectrogram feature

A novel data-driven non-intrusive method to assess speech intelligibility is proposed. The approach uses a new segment-based feature called Sum-Sorted Spectrogram (SSS) and a logistic regression network to predict the intelligibility score of degraded speech. Experiment results show that this approa...

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Bibliographic Details
Main Authors: Jia Xupeng, Li Dongmei
Format: Conference Proceeding
Language:English
Subjects:
data-driven
Feature extraction
intelligibility assessment
logistic regression
Logistics
Nickel alloys
non-intrusive
Silicides
Spectrogram
Speech
sum-sorted spectrogram
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Summary:A novel data-driven non-intrusive method to assess speech intelligibility is proposed. The approach uses a new segment-based feature called Sum-Sorted Spectrogram (SSS) and a logistic regression network to predict the intelligibility score of degraded speech. Experiment results show that this approach predicts speech intelligibility with an RMS error of 0.07 against short time objective intelligibility (STOI) index on a test database of noisy speech, and a Spearman Correlation Coefficient (SCC) of 0.98.
ISSN:2164-5221
DOI:10.1109/ICSP.2016.7877892