EVA: A New Framework for Evaluating Voice Agents

EVA (Evaluation of Voice Agents) is a novel framework designed to provide a holistic assessment of conversational voice agents. Unlike existing methods that focus on individual components or isolated aspects of performance, EVA evaluates complete, multi-turn spoken conversations using a realistic bot-to-bot architecture. This end-to-end approach allows for the joint scoring of task success (Accuracy) and conversational experience (Experience), represented by the EVA-A and EVA-X scores, respectively. The framework is accompanied by an initial airline dataset comprising 50 scenarios, covering tasks such as flight rebooking and cancellation handling. Benchmark results for 20 different systems, including cascade and audio-native models, reveal a consistent Accuracy-Experience tradeoff, highlighting the challenge of simultaneously optimizing for both dimensions. EVA aims to address the current lack of comprehensive evaluation frameworks in the field, which often assess individual components in isolation. By treating voice agent quality as an integrated whole, EVA enables developers to identify and address failures along both accuracy and experience dimensions, ultimately leading to the development of more effective and user-friendly voice-based AI systems. The release of the framework, code, dataset, and judge prompts is expected to foster innovation and collaboration within the research community.

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