Abstract
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.
Original language | English |
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Title of host publication | Proceedings of 58th Annual Meeting of the Association for Computational Linguistics |
Publication date | 2020 |
Pages | 2370-2380 |
Publication status | Published - 2020 |
Event | 58th Annual Meeting of the Association for Computational Linguistics - Onlibe event Duration: 6 Jul 2020 → 10 Jul 2020 https://acl2020.org/ |
Conference
Conference | 58th Annual Meeting of the Association for Computational Linguistics |
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Location | Onlibe event |
Period | 06/07/2020 → 10/07/2020 |
Internet address |