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

Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility