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。

多任务伪标签学习用于非侵入式语音质量评估模型

Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality  Assessment Model

Nowadays, most of the objective speech quality assessment tools (e.g.,
perceptual evaluation of speech quality (PESQ)) are based on the comparison of
the degraded/processed speech with its clean counterpart. The need of a
"golden" reference considerably restricts the practicality of such assessment
tools in real-world scenarios since the clean reference usually cannot be
accessed. On the other hand, human beings can readily evaluate the speech
quality without any reference (e.g., mean opinion score (MOS) tests), implying
the existence of an objective and non-intrusive (no clean reference needed)
quality assessment mechanism. In this study, we propose a novel end-to-end,
non-intrusive speech quality evaluation model, termed Quality-Net, based on
bidirectional long short-term memory. The evaluation of utterance-level quality
in Quality-Net is based on the frame-level assessment. Frame constraints and
sensible initializations of forget gate biases are applied to learn meaningful
frame-level quality assessment from the utterance-level quality label.
Experimental results show that Quality-Net can yield high correlation to PESQ
(0.9 for the noisy speech and 0.84 for the speech processed by speech
enhancement). We believe that Quality-Net has potential to be used in a wide
variety of applications of speech signal processing.

本研究提出了一种基于双向长短时记忆的全新端到端的非侵入式语音质量评估模型 Quality-Net，该模型可在没有干净参考语音的情况下对语音质量进行评估，实验结果表明 Quality-Net 可以产生与 PESQ 高相关性（噪声语音为 0.9，经过语音增强处理的语音为 0.84），有潜力在语音信号处理的广泛应用中使用。