Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning
Abstract
Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues. To address this, we introduce Sympatheia, a speech-to-speech dialogue framework conditioned on affect inferred from the user's speech and, when available, explicit affect specifications provided as a continuous valence--arousal (VA) control signal by a multimodal sensing module or user interface. To train our model, we construct Sympatheia-18k, an emotion-conditioned synthetic spoken dialogue corpus with 12 emotion anchors. This dataset includes an emotional split for learning affective speech behavior, and a neutral split that pairs emotionally neutral queries with multiple emotion-conditioned responses to isolate explicit emotion control in emotionally ambiguous cases. Empirical results show that Sympatheia outperforms speech conversational baselines in generating responses whose semantic content and spoken delivery are both emotionally appropriate. We further show that the same VA interface can integrate emotion estimates from diverse sensing modules, including facial expression, biosignals, and textual affect descriptions, improving response alignment when speech alone provides limited emotional evidence. These results suggest that continuous affect conditioning is an effective practical step for building emotionally adaptive voice assistants.
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 Sympatheia, a voice-native framework for emotionally aligned speech dialogue that integrates implicit and explicit affect conditioning. This work represents a significant advancement in the development of empathetic voice assistants, providing a comprehensive approach to generating emotionally appropriate responses in spoken dialogue systems. The combination of a novel dataset, robust methodology, and thorough evaluation underscores its importance in the field of machine learning and audio processing.
Methodology
The methodology presented in this paper is robust and innovative, combining implicit affect inference from user speech with explicit valence-arousal (VA) conditioning. The authors construct a novel dataset (Sympatheia-18k) that allows for the training of a speech-to-speech dialogue system capable of generating emotionally appropriate responses. The use of continuous VA coordinates as a conditioning mechanism is a significant advancement over traditional discrete emotion categories, allowing for more nuanced emotional responses. The integration of multimodal emotion sensing modules adds further depth to the system, making it adaptable to various input types. The architecture follows a well-established speech-language model (GLM-4-Voice) but enhances it with emotional conditioning, which is a thoughtful approach to improving empathetic dialogue systems.
Results & evaluation
The experimental evaluation is comprehensive, utilizing both automated and human assessments to evaluate the empathetic response quality of the Sympatheia system. The authors employ a variety of metrics, including empathy scores from an audio-capable LLM and a human Emotion Mean Opinion Score (MOS) study, which provides a well-rounded view of the model's performance. The results indicate that Sympatheia significantly outperforms baseline models in generating emotionally appropriate responses, validating the effectiveness of the proposed methods. The use of both emotional and neutral splits in the dataset allows for a thorough examination of the model's capabilities across different emotional contexts.
Reproducibility
The paper provides detailed implementation details, including training configurations and dataset generation processes, which enhance reproducibility. The availability of the project code and dataset on GitHub and Hugging Face respectively further supports the ability of other researchers to replicate the study. However, the reliance on synthetic data for training may introduce variability that could affect reproducibility in real-world applications.
Limitations
The paper acknowledges several limitations, including the synthetic nature of the training data, which may not fully capture the complexity of real-world conversations. Additionally, the fixed VA anchors used for emotional conditioning may not universally apply across different cultures or individual expressions of emotion. The authors also note that the current evaluation primarily relies on automated assessments, which may miss nuanced failures in empathy and appropriateness.
Broader impact
The potential applications of Sympatheia are significant, particularly in assistive technologies, education, and mental health support, where emotionally aware interactions can enhance user experience. However, the deployment of such systems raises ethical considerations regarding privacy and the potential for misuse in manipulative contexts. The authors emphasize the need for safeguards and responsible deployment practices to mitigate these risks. The main contribution of this paper is the introduction of Sympatheia, a voice-native framework for emotionally aligned speech dialogue that integrates implicit and explicit affect conditioning. This work represents a significant advancement in the development of empathetic voice assistants, providing a comprehensive approach to generating emotionally appropriate responses in spoken dialogue systems. The combination of a novel dataset, robust methodology, and thorough evaluation underscores its importance in the field of machine learning and audio processing.
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