  Speech intelligibility and quality assessment models are essential tools for
researchers to evaluate and improve speech processing models. However, only a
few studies have investigated multi-task models for intelligibility and quality
assessment due to the limitations of available data. In this study, we released
TMHINT-QI, the first Chinese speech dataset that records the quality and
intelligibility scores of clean, noisy, and enhanced utterances. Then, we
propose InQSS, a non-intrusive multi-task learning framework for
intelligibility and quality assessment. We evaluated the InQSS on both the
training-from-scratch and the pretrained models. The experimental results
confirm the effectiveness of the InQSS framework. In addition, the resulting
model can predict not only the intelligibility scores but also the quality
scores of a speech signal.
