Evaluating the Expressive Appropriateness of Speech in Rich Contexts
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper introduces a novel framework for evaluating expressive appropriateness in speech that integrates contextual understanding and advanced modeling techniques. The comprehensive approach, robust experimental validation, and potential for broad applications mark a significant contribution to the field of machine learning and audio processing.
Methodology
The paper presents a comprehensive methodology for evaluating expressive appropriateness in speech, which is a significant advancement over traditional methods that focus primarily on emotional intensity or isolated utterance characteristics. The introduction of the CEAEval framework, along with the CEAEval-D dataset and CEAEval-M model, showcases a well-structured approach that integrates knowledge distillation, adaptive audio attention bias, and reinforcement learning. The separation of the expressive planner and scoring model is particularly innovative, allowing for better handling of long-range contextual information. The use of a multi-dimensional annotation system for expressive attributes is a strong methodological contribution, ensuring a nuanced evaluation of speech expressiveness.
Results & evaluation
The experimental evaluation is robust, with a clear comparison against existing benchmarks. The authors provide detailed results demonstrating that CEAEval-M significantly outperforms other models across various context sizes. The use of human-annotated data enhances the credibility of the findings, and the reported metrics (LCC and ACC) effectively convey the model's performance. The ablation studies further strengthen the evaluation by isolating the impact of different components of the proposed framework.
Reproducibility
The paper outlines a clear methodology for data collection and model training, which supports reproducibility. However, the lack of publicly available raw audio data limits the ability of other researchers to fully replicate the study. The authors do commit to releasing model checkpoints and parameters, which is a positive step towards enhancing reproducibility.
Limitations
The study is primarily focused on Mandarin speech, which may limit its applicability to other languages and cultural contexts. The authors acknowledge this limitation and express intentions to expand the framework to additional languages in future work. Additionally, the subjective nature of expressive appropriateness could introduce variability in human annotations, although the high inter-annotator agreement suggests reliability.
Broader impact
The proposed framework has significant implications for various applications, including conversational agents, audiobook generation, and interactive storytelling systems. By providing a systematic way to evaluate expressive speech, this research could enhance user experiences in narrative-driven applications and contribute to the development of more emotionally aware AI systems. This paper introduces a novel framework for evaluating expressive appropriateness in speech that integrates contextual understanding and advanced modeling techniques. The comprehensive approach, robust experimental validation, and potential for broad applications mark a significant contribution to the field of machine learning and audio processing.
Author of this paper? Send a correction.