Without the need for a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. While deep learning models have been used to develop non-intrusive speech assessment methods with promising results, there is limited research on hearing-impaired subjects. This study proposes a multi-objective non-intrusive hearing-aid speech assessment model, called HASA-Net Large, which predicts speech quality and intelligibility scores based on input speech signals and specified hearing-loss patterns. Our experiments showed the utilization of pre-trained SSL models leads to a significant boost in speech quality and intelligibility predictions compared to using spectrograms as input. Additionally, we examined three distinct fine-tuning approaches that resulted in further performance improvements. Furthermore, we demonstrated that incorporating SSL models resulted in greater transferability to OOD dataset. Finally, this study introduces HASA-Net Large, which is a non-invasive approach for evaluating speech quality and intelligibility. HASA-Net Large utilizes raw waveforms and hearing-loss patterns to accurately predict speech quality and intelligibility levels for individuals with normal and impaired hearing and demonstrates superior prediction performance and transferability.

翻译：无需干净参考的非侵入式言语评估方法在客观评价中备受关注。尽管深度学习模型已被用于开发出具有良好效果的非侵入式言语评估方法，但针对听障受试者的研究仍然有限。本研究提出了一种名为HASA-Net Large的多目标非侵入式助听言语评估模型，该模型基于输入语音信号和指定的听力损失模式预测语音质量和可懂度评分。实验表明，与使用语谱图作为输入相比，利用预训练的SSL模型可显著提升语音质量和可懂度预测性能。此外，我们研究了三种不同的微调方法，这些方法进一步提升了性能。更进一步，我们证明了引入SSL模型可增强对分布外数据集的可迁移性。最后，本研究介绍了HASA-Net Large，这是一种评估语音质量和可懂度的非侵入式方法。HASA-Net Large利用原始波形和听力损失模式，准确预测正常听力与听力受损者的语音质量和可懂度水平，展现出优越的预测性能与可迁移性。