  Non-intrusive speech quality assessment is a crucial operation in multimedia
applications. The scarcity of annotated data and the lack of a reference signal
represent some of the main challenges for designing efficient quality
assessment metrics. In this paper, we propose two multi-task models to tackle
the problems above. In the first model, we first learn a feature representation
with a degradation classifier on a large dataset. Then we perform MOS
prediction and degradation classification simultaneously on a small dataset
annotated with MOS. In the second approach, the initial stage consists of
learning features with a deep clustering-based unsupervised feature
representation on the large dataset. Next, we perform MOS prediction and
cluster label classification simultaneously on a small dataset. The results
show that the deep clustering-based model outperforms the degradation
classifier-based model and the 3 baselines (autoencoder features, P.563, and
SRMRnorm) on TCD-VoIP. This paper indicates that multi-task learning combined
with feature representations from unlabelled data is a promising approach to
deal with the lack of large MOS annotated datasets.
