We address a challenging and practical task of labeling questions in speech in real time during telephone calls to emergency medical services in English, which embeds within a broader decision support system for emergency call-takers. We propose a novel multimodal approach to real-time sequence labeling in speech. Our model treats speech and its own textual representation as two separate modalities or views, as it jointly learns from streamed audio and its noisy transcription into text via automatic speech recognition. Our results show significant gains of jointly learning from the two modalities when compared to text or audio only, under adverse noise and limited volume of training data. The results generalize to medical symptoms detection where we observe a similar pattern of improvements with multimodal learning.
@article{arxiv.2005.00812,
title = {MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech},
author = {Jakob D. Havtorn and Jan Latko and Joakim Edin and Lasse Borgholt and Lars Maaløe and Lorenzo Belgrano and Nicolai F. Jacobsen and Regitze Sdun and Željko Agić},
journal= {arXiv preprint arXiv:2005.00812},
year = {2020}
}