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

arXiv: https://arxiv.org/abs/2307.04517 · HTML: https://mlsanity.com/paper/2307.04517/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 10 Jul 2023.

## Built on by (most cited first, 2 of 2)

- [P.808 Multilingual Speech Enhancement Testing: Approach and Results of URGENT 2025 Challenge](https://mlsanity.com/paper/2507.11306/) (2025)
- [ASR ensembling for phoneme intelligibility evaluation of speech anonymizers](https://mlsanity.com/paper/2609.28577/) (2026)

## Abstract (arXiv)

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.