# Evaluating the Expressive Appropriateness of Speech in Rich Contexts

arXiv: https://arxiv.org/abs/2605.09413 · HTML: https://mlsanity.com/paper/2605.09413/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2026.

## Built on by (most cited first, 3 of 3)

- [Diagnose, Then Refine: A Closed-Loop TTS System with AudioLLM-Guided Correction](https://mlsanity.com/paper/2608.28970/) (2026)
- [Multi-Dimensional Prosody Judgment For Live Streaming Speech Synthesis](https://mlsanity.com/paper/2609.20124/) (2026)
- [Can Prosodic Style Be Inferred from Text Alone? Evidence from Unsupervised Acoustic Clusters](https://mlsanity.com/paper/2610.05575/) (2026)

## Abstract (arXiv)

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.