No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction

• URL: http://arxiv.org/abs/2506.02039v1
• Date: Sat, 31 May 2025 07:55:03 GMT
• Title: No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction
• Authors: Haoshuai Zhou, Changgeng Mo, Boxuan Cao, Linkai Li, Shan Xiang Wang,
• Abstract summary: Previous approaches have mainly relied on audiograms, which are inherently limited in accuracy as they only capture a listener's hearing threshold for pure tones.<n>We propose a novel approach that leverages an individual's existing intelligibility data to predict their performance on new audio.<n>Our work presents a new paradigm for personalized speech intelligibility prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized speech intelligibility prediction is challenging. Previous approaches have mainly relied on audiograms, which are inherently limited in accuracy as they only capture a listener's hearing threshold for pure tones. Rather than incorporating additional listener features, we propose a novel approach that leverages an individual's existing intelligibility data to predict their performance on new audio. We introduce the Support Sample-Based Intelligibility Prediction Network (SSIPNet), a deep learning model that leverages speech foundation models to build a high-dimensional representation of a listener's speech recognition ability from multiple support (audio, score) pairs, enabling accurate predictions for unseen audio. Results on the Clarity Prediction Challenge dataset show that, even with a small number of support (audio, score) pairs, our method outperforms audiogram-based predictions. Our work presents a new paradigm for personalized speech intelligibility prediction.

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