  Subjective tests are the gold standard for evaluating speech quality and
intelligibility; however, they are time-consuming and expensive. Thus,
objective measures that align with human perceptions are crucial. This study
evaluates the correlation between commonly used objective measures and
subjective speech quality and intelligibility using a Chinese speech dataset.
Moreover, new objective measures are proposed that combine current objective
measures using deep learning techniques to predict subjective quality and
intelligibility. The proposed deep learning model reduces the amount of
training data without significantly affecting prediction performance. We
analyzed the deep learning model to understand how objective measures reflect
subjective quality and intelligibility. We also explored the impact of
including subjective speech quality ratings on speech intelligibility
prediction. Our findings offer valuable insights into the relationship between
objective measures and human perceptions.
