Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection
This addresses the issue of false alarms in emergency call-for-help detection systems, which is critical for real-world deployment, though it appears incremental as it builds on existing ASR models.
The paper tackled the problem of false alarms in call-for-help detection systems caused by environmental noise, proposing a noise-agnostic multitask learning approach that integrates noise classification into an ASR encoder, resulting in a significant reduction in false alarms and improved performance.
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.