In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously. Experimental results show that MOSA-Net can improve the linear correlation coefficient (LCC) by 0.026 (0.990 vs 0.964 in seen noise environments) and 0.012 (0.969 vs 0.957 in unseen noise environments) in perceptual evaluation of speech quality (PESQ) prediction, compared to Quality-Net, an existing single-task model for PESQ prediction, and improve LCC by 0.021 (0.985 vs 0.964 in seen noise environments) and 0.047 (0.836 vs 0.789 in unseen noise environments) in short-time objective intelligibility (STOI) prediction, compared to STOI-Net (based on CRNN), an existing single-task model for STOI prediction. Moreover, MOSA-Net, originally trained to assess objective scores, can be used as a pre-trained model to be effectively adapted to an assessment model for predicting subjective quality and intelligibility scores with a limited amount of training data. Experimental results show that MOSA-Net can improve LCC by 0.018 (0.805 vs 0.787) in mean opinion score (MOS) prediction, compared to MOS-SSL, a strong single-task model for MOS prediction. In light of the confirmed prediction capability, we further adopt the latent representations of MOSA-Net to guide the speech enhancement (SE) process and derive a quality-intelligibility (QI)-aware SE (QIA-SE) approach accordingly. Experimental results show that QIA-SE provides superior enhancement performance compared with the baseline SE system in terms of objective evaluation metrics and qualitative evaluation test. For example, QIA-SE can improve PESQ by 0.301 (2.953 vs 2.652 in seen noise environments) and 0.18 (2.658 vs 2.478 in unseen noise environments) over a CNN-based baseline SE model.

翻译：本研究提出了一种名为MOSA-Net的跨域多目标语音评估模型，能够同时估计多种语音评估指标。实验结果表明，在语音质量感知评估（PESQ）预测任务中，相较于现有的PESQ预测单任务模型Quality-Net，MOSA-Net在线性相关系数（LCC）上分别提升了0.026（已知噪声环境中0.990对比0.964）和0.012（未知噪声环境中0.969对比0.957）；在短时客观可懂度（STOI）预测任务中，相较于基于CRNN的现有STOI预测单任务模型STOI-Net，MOSA-Net在LCC上分别提升了0.021（已知噪声环境中0.985对比0.964）和0.047（未知噪声环境中0.836对比0.789）。此外，原本为客观评分评估训练的MOSA-Net，可作为预训练模型有效适配至主观质量与可懂度评分预测任务，且仅需少量训练数据。实验显示，在平均意见得分（MOS）预测任务中，相较于强单任务模型MOS-SSL，MOSA-Net的LCC提升了0.018（0.805对比0.787）。基于已验证的预测能力，我们进一步采用MOSA-Net的潜在表征来指导语音增强（SE）过程，据此提出了一种质量-可懂度（QI）感知的语音增强方法（QIA-SE）。实验结果表明，在客观评估指标和定性评估测试方面，QIA-SE相较于基线SE系统展现出更优的增强性能。例如，在基于CNN的基线SE模型基础上，QIA-SE在已知噪声环境中将PESQ提升了0.301（2.953对比2.652），在未知噪声环境中提升了0.18（2.658对比2.478）。