This study introduces multi-task pseudo-label (MPL) learning for a
non-intrusive speech quality assessment model. MPL consists of two stages which
are obtaining pseudo-label scores from a pretrained model and performing
multi-task learning. The 3QUEST metrics, namely Speech-MOS (S-MOS), Noise-MOS
(N-MOS), and General-MOS (G-MOS) are selected as the primary ground-truth
labels. Additionally, the pretrained MOSA-Net model is utilized to estimate
three pseudo-labels: perceptual evaluation of speech quality (PESQ), short-time
objective intelligibility (STOI), and speech distortion index (SDI). Multi-task
learning stage of MPL is then employed to train the MTQ-Net model (multi-target
speech quality assessment network). The model is optimized by incorporating
Loss supervision (derived from the difference between the estimated score and
the real ground-truth labels) and Loss semi-supervision (derived from the
difference between the estimated score and pseudo-labels), where Huber loss is
employed to calculate the loss function. Experimental results first demonstrate
the advantages of MPL compared to training the model from scratch and using
knowledge transfer mechanisms. Secondly, the benefits of Huber Loss in
improving the prediction model of MTQ-Net are verified. Finally, the MTQ-Net
with the MPL approach exhibits higher overall prediction capabilities when
compared to other SSL-based speech assessment models.

该研究介绍了多任务伪标签（MPL）学习在非侵入性语音质量评估模型中的应用，通过两个阶段获得伪标签分数和进行多任务学习，实验证明了 MPL 相较于从零开始训练模型和使用知识传递机制的优势，并验证了 Huber 损失函数在提高 MTQ-Net 预测模型的能力方面的益处，最终 MTQ-Net 采用 MPL 方法展示了较其他基于 SSL 的语音评估模型更高的整体预测能力。