Intelligibility Prediction for Time-Modified Speech Signals Using Spectro-Temporal Modulation Features
Year: 2025
Type: conference-paper
Abstract: Reference-based speech intelligibility prediction algorithms (RB-SIPAs) are limited to speech degradations that maintain the time alignment between the clean and degraded speech signals. We address this limitation by augmenting the existing RB-SIPAs framework with time alignment. To achieve robust time alignment at low signal-to-noise ratios, we propose using spectro-temporal modulations (STM) of the speech signals for dynamic time warping (DTW). Our experiments demonstrate that in DTW, the use of specific STM components as features for clean and time-modified degraded speech achieves the baseline time alignment obtained using clean speech and noise-free time-modified speech. Moreover, we propose two methods to incorporate time alignment into existing RB-SIPAs. Using these methods, we compare the output scores of the RB-SIPA with listening test scores and show better correlation results using the STM features as compared to MFCCs.
Source: Interspeech (USB)
Authors: Aymen Bashir, Haolan Wang, Amin Edraki, Wai-Yip Chan, Jesper Jensen
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415433487
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