Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups

• URL: http://arxiv.org/abs/2408.10516v1
• Date: Tue, 20 Aug 2024 03:33:04 GMT
• Title: Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups
• Authors: Zhiyang Qi, Michimasa Inaba,
• Abstract summary: This study addresses the interaction challenges encountered by spoken dialogue systems (SDSs) when engaging with users who exhibit distinct conversational behaviors. We propose a novel data augmentation framework to enhance SDS performance for user groups with limited resources.
• Score: 1.7725414095035827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study addresses the interaction challenges encountered by spoken dialogue systems (SDSs) when engaging with users who exhibit distinct conversational behaviors, particularly minors, in scenarios where data are scarce. We propose a novel data augmentation framework to enhance SDS performance for user groups with limited resources. Our approach leverages a large language model (LLM) to extract speaker styles and a pre-trained language model (PLM) to simulate dialogue act history. This method generates enriched and personalized dialogue data, facilitating improved interactions with unique user demographics. Extensive experiments validate the efficacy of our methodology, highlighting its potential to foster the development of more adaptive and inclusive dialogue systems.

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