24.4 MOVING SPEECH TECHNOLOGY METHODS OUT OF THE LABORATORY: PRACTICAL CHALLENGES AND CLINICAL TRANSLATION OPPORTUNITIES FOR PSYCHIATRY. (9th April 2019)

• Record Type: Journal Article
• Authors: Holmlund, Terje; Foltz, Peter W; Cohen, Alex S; Cheng, Jian; Bernstein, Jared; Rosenfeld, Elizabeth; Elvevåg, Brita
• Is Part Of: Schizophrenia bulletin. Volume 45(2019)Supplement 2
• Page Start: S129
• Page End: S129
• Publication Date: 2019-04-09
• Subjects: Schizophrenia -- Periodicals; Schizophrenia -- Research -- Periodicals; 616.898005
• DOI: 10.1093/schbul/sbz022.099
• Languages: English
• ISSNs: 0586-7614

Abstract

Abstract: Background: Psychiatric patients, such as those suffering from depression or schizophrenia, often need to be monitored with frequent clinical interviews by trained professionals so as to avoid costly emergency care and unfortunate events (e.g., suicide attempts). Technological advances in the form of smart devices offer a mobile platform through which to provide the effective and affordable monitoring of clinical events. Novel speech technologies offer promise of increased efficiency, sensitivity and objectivity by the implementation of automatic speech recognition and natural language processing methods to facilitate in the tracking of the clinical state of psychiatric outpatients longitudinally and, when appropriate, alerting clinical staff to contact that patient. However, thus far most research that has leveraged such technology has been conducted within controlled laboratory or clinical environments, and as such it remains unknown how robust such methods would be when the data collection is in uncontrolled settings and controlled by the participants themselves. Yet if these methods are to truly have clinical translation value then they must be demonstrated to be user-engineered to nurture participation and to be tolerated by participants despite frequent use, and that the resulting behavioral responses - notably voice - that are collected in uncontrolled settings remain interpretable by speech recognition and natural language processing methods. Methods: We developed a mobile tool that enabled participants to remotely self-administer daily interactions through a smart device. The application engaged participants in spoken and touch-based interactions to assess cognition, motor skill, and language. The speech samples were analyzed using recent technological advances in speech and language processing to recognize both the content and patterns in the speech. Our study was conducted in both the United States and in Norway, and thus occurred within different languages as well as cultural and legal settings. A total of 353 participants used the software application over three data collection trials. Of these, 219 were healthy volunteers and 134 were patients with a range of diagnoses of psychosis spectrum disorders, substance abuse disorders, and affective disorders. Results: This talk will explore our experience of leveraging technological advances to move assessment of cognition, motor skill, and language out of the controlled laboratory and into real-world settings. We will discuss the necessity of excellent usability engineering, and the complex data security issues that arise with speech specifically, especially when data collection and analysis can - either intentionally or unintentionally - cross international borders. We will also illustrate the challenges of creating a clinically useful analytic framework for the numerous channels of data that now have the added temporal dimension. Conclusions: In sum, although new technological frameworks - that leverage speech technology and natural language processing methods - provide unprecedented opportunities for remotely monitoring behavior, the challenge of creating a useful analytic framework for clinical purposes remains.