@inproceedings{wang-etal-2026-evaluating,
title = "Evaluating the Expressive Appropriateness of Speech in Rich Contexts",
author = "Wang, Tianrui and
Ma, Ziyang and
Peng, Yizhou and
Wang, Haoyu and
Niu, Zhikang and
Huang, Zikang and
Wu, Yihao and
Chao, Yi-Wen and
Jiang, Yu and
Lu, Yuheng and
Yang, Guanrou and
Li, Xuanchen and
Liu, Hexin and
Qiang, Chunyu and
Gong, Cheng and
Yang, Yifan and
Liu, Tianchi and
Wang, Junyu and
Hou, Nana and
Ge, Meng and
You, Fuming and
Wei, Yang and
Sun, Zhongqian and
Haifeng, Hu and
Wang, Xiaobao and
Chng, Eng Siong and
Chen, Xie and
Wang, Longbiao and
Dang, Jianwu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.411/",
pages = "9088--9106",
ISBN = "979-8-89176-390-6",
abstract = "Evaluating expressive speech remains challenging, as existing methods mainly assess emotional intensity and overlook whether a speech sample is expressively appropriate for its contextual setting. This limitation hinders reliable evaluation of speech systems used in narrative-driven and interactive applications, such as audiobooks and conversational agents. We introduce CEAEval, a Context-rich framework for Evaluating Expressive Appropriateness in speech, which assesses whether a speech sample expressively aligns with the underlying communicative intent implied by its discourse-level narrative context. To support this task, we construct CEAEval-D, the first context-rich speech dataset with real human performances in Mandarin conversational speech, providing narrative descriptions together with fifteen dimensions of human annotations covering expressive attributes and expressive appropriateness. We further develop CEAEval-M, a model that integrates knowledge distillation, planner-based multi-model collaboration, adaptive audio attention bias, and reinforcement learning to perform context-rich expressive appropriateness evaluation. Experiments on a human-annotated test set demonstrate that CEAEval-M substantially outperforms existing speech evaluation and analysis systems."
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<abstract>Evaluating expressive speech remains challenging, as existing methods mainly assess emotional intensity and overlook whether a speech sample is expressively appropriate for its contextual setting. This limitation hinders reliable evaluation of speech systems used in narrative-driven and interactive applications, such as audiobooks and conversational agents. We introduce CEAEval, a Context-rich framework for Evaluating Expressive Appropriateness in speech, which assesses whether a speech sample expressively aligns with the underlying communicative intent implied by its discourse-level narrative context. To support this task, we construct CEAEval-D, the first context-rich speech dataset with real human performances in Mandarin conversational speech, providing narrative descriptions together with fifteen dimensions of human annotations covering expressive attributes and expressive appropriateness. We further develop CEAEval-M, a model that integrates knowledge distillation, planner-based multi-model collaboration, adaptive audio attention bias, and reinforcement learning to perform context-rich expressive appropriateness evaluation. Experiments on a human-annotated test set demonstrate that CEAEval-M substantially outperforms existing speech evaluation and analysis systems.</abstract>
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%0 Conference Proceedings
%T Evaluating the Expressive Appropriateness of Speech in Rich Contexts
%A Wang, Tianrui
%A Ma, Ziyang
%A Peng, Yizhou
%A Wang, Haoyu
%A Niu, Zhikang
%A Huang, Zikang
%A Wu, Yihao
%A Chao, Yi-Wen
%A Jiang, Yu
%A Lu, Yuheng
%A Yang, Guanrou
%A Li, Xuanchen
%A Liu, Hexin
%A Qiang, Chunyu
%A Gong, Cheng
%A Yang, Yifan
%A Liu, Tianchi
%A Wang, Junyu
%A Hou, Nana
%A Ge, Meng
%A You, Fuming
%A Wei, Yang
%A Sun, Zhongqian
%A Haifeng, Hu
%A Wang, Xiaobao
%A Chng, Eng Siong
%A Chen, Xie
%A Wang, Longbiao
%A Dang, Jianwu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-evaluating
%X Evaluating expressive speech remains challenging, as existing methods mainly assess emotional intensity and overlook whether a speech sample is expressively appropriate for its contextual setting. This limitation hinders reliable evaluation of speech systems used in narrative-driven and interactive applications, such as audiobooks and conversational agents. We introduce CEAEval, a Context-rich framework for Evaluating Expressive Appropriateness in speech, which assesses whether a speech sample expressively aligns with the underlying communicative intent implied by its discourse-level narrative context. To support this task, we construct CEAEval-D, the first context-rich speech dataset with real human performances in Mandarin conversational speech, providing narrative descriptions together with fifteen dimensions of human annotations covering expressive attributes and expressive appropriateness. We further develop CEAEval-M, a model that integrates knowledge distillation, planner-based multi-model collaboration, adaptive audio attention bias, and reinforcement learning to perform context-rich expressive appropriateness evaluation. Experiments on a human-annotated test set demonstrate that CEAEval-M substantially outperforms existing speech evaluation and analysis systems.
%U https://aclanthology.org/2026.acl-long.411/
%P 9088-9106
Markdown (Informal)
[Evaluating the Expressive Appropriateness of Speech in Rich Contexts](https://aclanthology.org/2026.acl-long.411/) (Wang et al., ACL 2026)
ACL
- Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, and Jianwu Dang. 2026. Evaluating the Expressive Appropriateness of Speech in Rich Contexts. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9088–9106, San Diego, California, United States. Association for Computational Linguistics.