Perceptual speech quality is an important performance metric for
teleconferencing applications. The mean opinion score (MOS) is standardized for
the perceptual evaluation of speech quality and is obtained by asking listeners
to rate the quality of a speech sample. Recently, there has been increasing
research interest in developing models for estimating MOS blindly. Here we
propose a multi-task framework to include additional labels and data in
training to improve the performance of a blind MOS estimation model.
Experimental results indicate that the proposed model can be trained to jointly
estimate MOS, reverberation time (T60), and clarity (C50) by combining two
disjoint data sets in training, one containing only MOS labels and the other
containing only T60 and C50 labels. Furthermore, we use a semi-supervised
framework to combine two MOS data sets in training, one containing only MOS
labels (per ITU-T Recommendation P.808), and the other containing separate
scores for speech signal, background noise, and overall quality (per ITU-T
Recommendation P.835). Finally, we present preliminary results for addressing
individual rater bias in the MOS labels.

本文提出了一种多任务框架来改进盲目 MOS 评估模型的性能，通过在训练中加入附加标签和数据，结合两个不连续的数据集来联合估算 MOS、混响时间和清晰度，并使用半监督框架来组合两个 MOS 数据集以解决评估者偏差。