Study on the Correlation Between Objective Evaluations and Subjective Speech Quality and Intelligibility
- Resource Type
- Conference
- Authors
- Chiang, Hsin-Tien; Hung, Kuo-Hsuan; Fu, Szu-Wei; Kuo, Heng-Cheng; Tsai, Ming-Hsueh; Tsao, Yu
- Source
- 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) Automatic Speech Recognition and Understanding Workshop (ASRU), 2023 IEEE. :1-7 Dec, 2023
- Subject
- Signal Processing and Analysis
Deep learning
Analytical models
Correlation
Current measurement
Conferences
Training data
Speech enhancement
Objective measures
subjective listening tests
speech quality
speech intelligibility
- Language
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.