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Deep attentive end-to-end continuous breath sensing from speech

MacIntyre, AD; Rizos, G; Batliner, A; Baird, A; Amiriparian, S; Hamilton, A; Schuller, BW; (2020) Deep attentive end-to-end continuous breath sensing from speech. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. (pp. pp. 2082-2086). ISCA: Virtual event. Green open access

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Abstract

Modelling of the breath signal is of high interest to both healthcare professionals and computer scientists, as a source of diagnosis-related information, or a means for curating higher quality datasets in speech analysis research. The formation of a breath signal gold standard is, however, not a straightforward task, as it requires specialised equipment, human annotation budget, and even then, it corresponds to lab recording settings, that are not reproducible in-the-wild. Herein, we explore deep learning based methodologies, as an automatic way to predict a continuous-time breath signal by solely analysing spontaneous speech. We address two task formulations, those of continuousvalued signal prediction, as well as inhalation event prediction, that are of great use in various healthcare and Automatic Speech Recognition applications, and showcase results that outperform current baselines. Most importantly, we also perform an initial exploration into explaining which parts of the input audio signal are important with respect to the prediction.

Type: Proceedings paper
Title: Deep attentive end-to-end continuous breath sensing from speech
Event: Annual Conference of the International Speech Communication Association, INTERSPEECH 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.21437/Interspeech.2020-2832
Publisher version: https://doi.org/10.21437/Interspeech.2020-2832
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: breath prediction from speech, end-to-end deep learning, neural attention, biological signal monitoring
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10118941
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