Optimizing Conversational Quality in Spoken Dialogue Systems with Reinforcement Learning from AI Feedback
Abstract
Reinforcement learning from human or AI feedback (RLHF/RLAIF) for speech-in/speech-out dialogue systems (SDS) remains underexplored, with prior work largely limited to single semantic rewards applied at the utterance level. Such setups overlook the multi-dimensional and multi-modal nature of conversational quality, which encompasses semantic coherence, audio naturalness, speaker consistency, emotion alignment, and turn-taking behavior. Moreover, they are fundamentally mismatched with duplex spoken dialogue systems that generate responses incrementally, where agents must make decisions based on partial utterances. We address these limitations with the first multi-reward RLAIF framework for SDS, combining semantic, audio-quality, and emotion-consistency rewards. To align utterance-level preferences with incremental, blockwise decoding in duplex models, we apply turn-level preference sampling and aggregate per-block log-probabilities within a single DPO objective. We present the first systematic study of preference learning for improving SDS quality in both multi-turn Chain-of-Thought and blockwise duplex models, and release a multi-reward DPO dataset to support reproducible research. Experiments show that single-reward RLAIF selectively improves its targeted metric, while joint multi-reward training yields consistent gains across semantic quality and audio naturalness. These results highlight the importance of holistic, multi-reward alignment for practical conversational SDS.
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 paper presents a pioneering multi-reward RLAIF framework for optimizing conversational quality in spoken dialogue systems, significantly advancing the field by integrating diverse quality dimensions into a cohesive training approach. The comprehensive methodology and robust experimental validation highlight its potential to improve user interaction in real-world applications.
Methodology
The paper introduces a multi-reward reinforcement learning framework for optimizing spoken dialogue systems (SDS), addressing the limitations of existing methods that focus on single semantic rewards. The authors propose a novel approach that incorporates multiple dimensions of conversational quality—semantic coherence, audio naturalness, intelligibility, and emotion consistency—through a unified preference-learning pipeline. This is achieved by utilizing Direct Preference Optimization (DPO) to align utterance-level preferences with incremental, blockwise decoding in duplex models. The methodology is well-structured, with clear definitions of reward functions and a systematic approach to preference pair construction that enhances the robustness of the model.
Results & evaluation
The experiments are comprehensive, utilizing a large-scale multi-reward DPO dataset constructed from real conversational contexts. The authors evaluate their proposed framework against established baselines, demonstrating significant improvements in both semantic and acoustic metrics. The results are statistically validated, showing that the joint multi-reward training approach consistently outperforms single-reward configurations. The use of various metrics, including LLM judge scores, ROUGE, and WER, provides a well-rounded evaluation of the model's performance across different dimensions of conversational quality.
Reproducibility
The authors commit to releasing their dataset and code, which is essential for reproducibility. However, the paper could benefit from more detailed implementation specifics, such as hyperparameter settings and training protocols, to facilitate replication by other researchers. While the methodology is described in detail, providing access to the actual code and data would enhance the ability of others to reproduce the results.
Limitations
The paper acknowledges several limitations, including potential biases introduced by automatic evaluators and the challenge of balancing multiple reward signals in practice. Additionally, the focus on English conversational datasets may limit the applicability of the findings to multilingual contexts. The authors also note that their approach relies on automatic preference data construction, which may not fully capture the nuances of human judgment.
Broader impact
This research has significant implications for the development of more sophisticated and human-like spoken dialogue systems, which could enhance user experiences in applications ranging from virtual assistants to customer service bots. By addressing the multi-dimensional nature of conversational quality, the proposed framework could lead to more engaging and effective interactions between humans and machines. The paper presents a pioneering multi-reward RLAIF framework for optimizing conversational quality in spoken dialogue systems, significantly advancing the field by integrating diverse quality dimensions into a cohesive training approach. The comprehensive methodology and robust experimental validation highlight its potential to improve user interaction in real-world applications.
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