@inproceedings{wang-etal-2025-qualispeech,
title = "{Q}uali{S}peech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions",
author = "Wang, Siyin and
Yu, Wenyi and
Chen, Xianzhao and
Tian, Xiaohai and
Zhang, Jun and
Lu, Lu and
Tsao, Yu and
Yamagishi, Junichi and
Wang, Yuxuan and
Zhang, Chao",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1150/",
doi = "10.18653/v1/2025.acl-long.1150",
pages = "23588--23609",
ISBN = "979-8-89176-251-0",
abstract = "This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical scoring methods. Natural language feedback provides instructive recommendations and detailed evaluations, yet existing datasets lack the comprehensive annotations needed for this approach. To bridge this gap, we introduce QualiSpeech, a comprehensive low-level speech quality assessment dataset encompassing 11 key aspects and detailed natural language comments that include reasoning and contextual insights. Additionally, we propose the QualiSpeech Benchmark to evaluate the low-level speech understanding capabilities of auditory large language models (LLMs). Experimental results demonstrate that finetuned auditory LLMs can reliably generate detailed descriptions of noise and distortion, effectively identifying their types and temporal characteristics. The results further highlight the potential for incorporating reasoning to enhance the accuracy and reliability of quality assessments. The dataset can be found at https://huggingface.co/datasets/tsinghua-ee/QualiSpeech."
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<abstract>This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical scoring methods. Natural language feedback provides instructive recommendations and detailed evaluations, yet existing datasets lack the comprehensive annotations needed for this approach. To bridge this gap, we introduce QualiSpeech, a comprehensive low-level speech quality assessment dataset encompassing 11 key aspects and detailed natural language comments that include reasoning and contextual insights. Additionally, we propose the QualiSpeech Benchmark to evaluate the low-level speech understanding capabilities of auditory large language models (LLMs). Experimental results demonstrate that finetuned auditory LLMs can reliably generate detailed descriptions of noise and distortion, effectively identifying their types and temporal characteristics. The results further highlight the potential for incorporating reasoning to enhance the accuracy and reliability of quality assessments. The dataset can be found at https://huggingface.co/datasets/tsinghua-ee/QualiSpeech.</abstract>
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%0 Conference Proceedings
%T QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions
%A Wang, Siyin
%A Yu, Wenyi
%A Chen, Xianzhao
%A Tian, Xiaohai
%A Zhang, Jun
%A Lu, Lu
%A Tsao, Yu
%A Yamagishi, Junichi
%A Wang, Yuxuan
%A Zhang, Chao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-qualispeech
%X This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical scoring methods. Natural language feedback provides instructive recommendations and detailed evaluations, yet existing datasets lack the comprehensive annotations needed for this approach. To bridge this gap, we introduce QualiSpeech, a comprehensive low-level speech quality assessment dataset encompassing 11 key aspects and detailed natural language comments that include reasoning and contextual insights. Additionally, we propose the QualiSpeech Benchmark to evaluate the low-level speech understanding capabilities of auditory large language models (LLMs). Experimental results demonstrate that finetuned auditory LLMs can reliably generate detailed descriptions of noise and distortion, effectively identifying their types and temporal characteristics. The results further highlight the potential for incorporating reasoning to enhance the accuracy and reliability of quality assessments. The dataset can be found at https://huggingface.co/datasets/tsinghua-ee/QualiSpeech.
%R 10.18653/v1/2025.acl-long.1150
%U https://aclanthology.org/2025.acl-long.1150/
%U https://doi.org/10.18653/v1/2025.acl-long.1150
%P 23588-23609
Markdown (Informal)
[QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions](https://aclanthology.org/2025.acl-long.1150/) (Wang et al., ACL 2025)
ACL
- Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Yu Tsao, Junichi Yamagishi, Yuxuan Wang, and Chao Zhang. 2025. QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23588–23609, Vienna, Austria. Association for Computational Linguistics.