Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection

• URL: http://arxiv.org/abs/2501.11631v1
• Date: Mon, 20 Jan 2025 18:01:42 GMT
• Title: Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection
• Authors: Myeonghoon Ryu, June-Woo Kim, Minseok Oh, Suji Lee, Han Park,
• Abstract summary: We propose a novel multitask learning approach that integrates a noise classification head into the ASR encoder. Our method enhances the model's robustness to noisy environments, leading to a significant reduction in false alarms and improved overall call-for-help performance.
• Score: 0.0
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• Abstract: Keyword spotting is often implemented by keyword classifier to the encoder in acoustic models, enabling the classification of predefined or open vocabulary keywords. Although keyword spotting is a crucial task in various applications and can be extended to call-for-help detection in emergencies, however, the previous method often suffers from scalability limitations due to retraining required to introduce new keywords or adapt to changing contexts. We explore a simple yet effective approach that leverages off-the-shelf pretrained ASR models to address these challenges, especially in call-for-help detection scenarios. Furthermore, we observed a substantial increase in false alarms when deploying call-for-help detection system in real-world scenarios due to noise introduced by microphones or different environments. To address this, we propose a novel noise-agnostic multitask learning approach that integrates a noise classification head into the ASR encoder. Our method enhances the model's robustness to noisy environments, leading to a significant reduction in false alarms and improved overall call-for-help performance. Despite the added complexity of multitask learning, our approach is computationally efficient and provides a promising solution for call-for-help detection in real-world scenarios.

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