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arXiv · 2412.15995

Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling

Maximillian Chen·Ruoxi Sun·Sercan Ö. Arık
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Abstract

Conversational assistants are increasingly popular across diverse real-world applications, highlighting the need for advanced multimodal speech modeling. Speech, as a natural mode of communication, encodes rich user-specific characteristics such as speaking rate and pitch, making it critical for effective interaction. Our work introduces a data-centric customization approach for efficiently enhancing multimodal understanding in conversational speech modeling. Central to our contributions is a novel multi-task learning paradigm that involves designing auxiliary tasks to utilize a small amount of speech data. Our approach achieves state-of-the-art performance on the Spoken-SQuAD benchmark, using only 10% of the training data with open-weight models, establishing a robust and efficient framework for audio-centric conversational modeling. We also introduce ASK-QA, the first dataset for multi-turn spoken dialogue with ambiguous user requests and dynamic evaluation inputs. Code and data forthcoming.

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Maximillian Chen, Ruoxi Sun, Sercan Ö. Arık. 2024-12-20. Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling. https://arxiv.org/abs/2412.15995

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