Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking

• URL: http://arxiv.org/abs/2409.06263v1
• Date: Tue, 10 Sep 2024 07:06:40 GMT
• Title: Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking
• Authors: Jihyun Lee, Solee Im, Wonjun Lee, Gary Geunbae Lee,
• Abstract summary: We introduce a simple yet effective data augmentation method to improve robustness of Dialogue State Tracking model. Our method generates sufficient error patterns on keywords, leading to improved accuracy in noised and low-accuracy ASR environments.
• Score: 17.96115263146684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialogue State Tracking (DST) is a key part of task-oriented dialogue systems, identifying important information in conversations. However, its accuracy drops significantly in spoken dialogue environments due to named entity errors from Automatic Speech Recognition (ASR) systems. We introduce a simple yet effective data augmentation method that targets those entities to improve the robustness of DST model. Our novel method can control the placement of errors using keyword-highlighted prompts while introducing phonetically similar errors. As a result, our method generated sufficient error patterns on keywords, leading to improved accuracy in noised and low-accuracy ASR environments.

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