Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e.g. as feature extractor for speech quality (SQ) prediction, which is, in turn, relevant for assessment and training speech enhancement systems for users with normal or impaired hearing. However, exact knowledge of why and how quality-related information is encoded well in such representations remains poorly understood. In this work, techniques for non-intrusive prediction of SQ ratings are extended to the prediction of intelligibility for hearing-impaired users. It is found that self-supervised representations are useful as input features to non-intrusive prediction models, achieving competitive performance to more complex systems. A detailed analysis of the performance depending on Clarity Prediction Challenge 1 listeners and enhancement systems indicates that more data might be needed to allow generalisation to unknown systems and (hearing-impaired) individuals

翻译：自监督语音表征已成功应用于多项语音处理任务，例如作为语音质量预测的特征提取器，而这又与正常听力及听力受损用户的语音增强系统评估与训练密切相关。然而，关于此类表征何以有效编码质量相关信息的精确机制仍不清晰。本研究将非侵入式语音质量评级预测技术拓展至听力受损用户的可懂度预测领域。研究发现，自监督表征作为非侵入式预测模型的输入特征具有实用价值，其性能可媲美更复杂的系统。基于清晰度预测挑战赛第一轮听众及增强系统的性能依赖分析表明，需要更多数据才能实现对未知系统及（听力受损）个体的泛化。