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Multi-instance Learning for Bipolar Disorder Diagnosis using Weakly Labelled Speech Data

Ren, Zhao, Han, Jing, Cummins, Nicholas, Kong, Qiuqiang, Plumbley, Mark and Schuller, Björn W. (2019) Multi-instance Learning for Bipolar Disorder Diagnosis using Weakly Labelled Speech Data In: DPH 2019: 9th International Digital Public Health Conference 2019, 20-23 Nov 2019, Marseille, France.

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Abstract

While deep learning is undoubtedly the predominant learning technique across speech processing, it is still not widely used in health-based applications. The corpora available for health-style recognition problems are often small, both concerning the total amount of data available and the number of individuals present. The Bipolar Disorder corpus, used in the 2018 Audio/Visual Emotion Challenge, contains only 218 audio samples from 46 individuals. Herein, we present a multi-instance learning framework aimed at constructing more reliable deep learning-based models in such conditions. First, we segment the speech files into multiple chunks. However, the problem is that each of the individual chunks is weakly labelled, as they are annotated with the label of the corresponding speech file, but may not be indicative of that label. We then train the deep learning-based (ensemble) multi-instance learning model, aiming at solving such a weakly labelled problem. The presented results demonstrate that this approach can improve the accuracy of feedforward, recurrent, and convolutional neural nets on the 3-class mania classification tasks undertaken on the Bipolar Disorder corpus.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Ren, Zhao
Han, Jing
Cummins, Nicholas
Kong, Qiuqiangq.kong@surrey.ac.uk
Plumbley, Markm.plumbley@surrey.ac.uk
Schuller, Björn W.
Date : 2019
Funders : European Union's Horizon 2020, Engineering and Physical Sciences Research Council (EPSRC), EFPIA
Grant Title : Marie Skłodowska-Curie grant agreement
Copyright Disclaimer : © 2019 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
Uncontrolled Keywords : Bipolar Disorder; Weakly Labelled Data; Multi-instance Learning
Related URLs :
Depositing User : Clive Harris
Date Deposited : 05 Nov 2019 11:51
Last Modified : 20 Nov 2019 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/853036

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