Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder

John Gideon, Emily Mower Provost, Melvin McInnis

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Speech contains patterns that can be altered by the mood of an individual. There is an increasing focus on automated and distributed methods to collect and monitor speech from large groups of patients suffering from mental health disorders. However, as the scope of these collections increases, the variability in the data also increases. This variability is due in part to the range in the quality of the devices, which in turn affects the quality of the recorded data, negatively impacting the accuracy of automatic assessment. It is necessary to mitigate variability effects in order to expand the impact of these technologies. This paper explores speech collected from phone recordings for analysis of mood in individuals with bipolar disorder. Two different phones with varying amounts of clipping, loudness, and noise are employed. We describe methodologies for use during preprocessing, feature extraction, and data modeling to correct these differences and make the devices more comparable. The results demonstrate that these pipeline modifications result in statistically significantly higher performance, which highlights the potential of distributed mental health systems.

    Original languageEnglish (US)
    Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2359-2363
    Number of pages5
    ISBN (Electronic)9781479999880
    DOIs
    StatePublished - May 18 2016
    Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
    Duration: Mar 20 2016Mar 25 2016

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Volume2016-May
    ISSN (Print)1520-6149

    Other

    Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
    CountryChina
    CityShanghai
    Period3/20/163/25/16

    Keywords

    • Bipolar Disorder
    • Mobile Health
    • Mood Modeling
    • Speech Analysis

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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