Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness
Abstract
The rapid evolution of end-to-end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances and the spontaneous nature of human conversation. However, current methods struggle with two critical gaps: the modality gap, involving prosody and emotion, and the colloquialness gap, distinguishing written scripts from natural speech. To address these challenges, we introduce SDiaReward, an end-to-end multi-turn reward model trained on SDiaReward-Dataset, a novel collection of episode-level preference pairs explicitly targeting these gaps. It operates directly on full multi-turn speech episodes and is optimized with pairwise preference supervision, enabling joint assessment of modality and colloquialness in a single evaluator. We further establish ESDR-Bench, a stratified benchmark for robust episode-level evaluation. Experiments demonstrate that SDiaReward achieves state-of-the-art pairwise preference accuracy, significantly outperforming general-purpose audio LLMs. Further analysis suggests that SDiaReward captures relative conversational expressiveness beyond superficial synthesis cues, improving generalization across domains and recording conditions. Code, data, and demos are available at https://sdiareward.github.io/.
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
The main contribution of this paper is the introduction of a novel reward modeling framework, SDiaReward, which significantly improves the evaluation of spoken dialogue systems by addressing modality and colloquialness gaps through a data-driven approach. This work is pivotal in advancing the state-of-the-art in dialogue systems, providing a comprehensive methodology and robust experimental validation that could influence future research and applications in the field.
Methodology
The paper introduces a novel reward modeling framework, SDiaReward, which effectively addresses the modality and colloquialness gaps in spoken dialogue systems. The methodology is well-structured, utilizing a pairwise preference learning approach to train on a specifically curated dataset (SDiaReward-Dataset) that captures the nuances of natural speech. The integration of multimodal LLMs for reward prediction, along with the establishment of ESDR-Bench for benchmarking, showcases a comprehensive approach to improving dialogue evaluation metrics.
Results & evaluation
The experimental evaluation is robust, demonstrating the superiority of SDiaReward over existing general-purpose audio LLMs through a series of well-defined metrics, including pairwise preference accuracy across various datasets. The results indicate significant improvements in capturing paralinguistic features and conversational spontaneity, which are critical for realistic spoken dialogue systems. The use of both micro and macro averages for accuracy assessment provides a nuanced understanding of model performance across different data regimes.
Reproducibility
The paper includes detailed implementation specifics, including model architecture, training procedures, and dataset construction methods. The availability of code and data enhances reproducibility, although the actual primary institution is not specified, which could limit the ability to verify institutional affiliations and resources.
Limitations
The paper acknowledges limitations related to the dataset's focus on "in-the-wild" recordings, which may affect the model's robustness in more controlled environments. Additionally, the potential for domain-specific biases in reward scoring is noted, suggesting that further refinements are necessary for broader applicability.
Broader impact
The work has significant implications for the development of more natural and effective spoken dialogue systems, which can enhance human-AI interactions across various applications, including customer service, education, and entertainment. By addressing critical gaps in existing models, this research paves the way for future advancements in dialogue systems that can better understand and generate human-like speech. The main contribution of this paper is the introduction of a novel reward modeling framework, SDiaReward, which significantly improves the evaluation of spoken dialogue systems by addressing modality and colloquialness gaps through a data-driven approach. This work is pivotal in advancing the state-of-the-art in dialogue systems, providing a comprehensive methodology and robust experimental validation that could influence future research and applications in the field.
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