Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study

Abstract

Background The prevalence of mental disorders worldwide is very high. The guideline-oriented care of patients depends on early diagnosis and regular and valid evaluation of their treatment to be able to quickly intervene should the patient’s mental health deteriorate. To ensure effective treatment, the level of experience of the physician or therapist is of importance, both in the initial diagnosis and in the treatment of mental illnesses. Nevertheless, experienced physicians and psychotherapists are not available in enough numbers everywhere, especially in rural areas or in less developed countries. Human speech can reveal a speaker’s mental state by altering its noncontent aspects (speech melody, intonations, speech rate, etc). This is noticeable in both the clinic and everyday life by having prior knowledge of the normal speech patterns of the affected person, and with enough time spent listening to the patient. However, this time and experience are often unavailable, leaving unused opportunities to capture linguistic, noncontent information. To improve the care of patients with mental disorders, we have developed a concept for assessing their most important mental parameters through a noncontent analysis of their active speech. Using speech analysis for the assessment and tracking of mental health patients opens up the possibility of remote, automatic, and ongoing evaluation when used with patients’ smartphones, as part of the current trends toward the increasing use of digital and mobile health tools. Objective The primary objective of this study is to evaluate measurements of participants' mental state by comparing the analysis of noncontent speech parameters to the results of several psychological questionnaires (Symptom Checklist-90 [SCL-90], the Patient Health Questionnaire [PHQ], and the Big 5 Test). Methods In this paper, we described a case-controlled study (with a case group and one control group). The participants will be recruited in an outpatient neuropsychiatric treatment center. Inclusion criteria are a neurological or psychiatric diagnosis made by a specialist, no terminal or life-threatening illnesses, and fluent use of the German language. Exclusion criteria include psychosis, dementia, speech or language disorders in neurological diseases, addiction history, a suicide attempt recently or in the last 12 months, or insufficient language skills. The measuring instrument will be the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters, and the assessment of findings using psychometric instruments and questionnaires (SCL-90, PHQ, Big 5 Test). Results The study is ongoing as of September 2019, but we have enrolled 254 participants. There have been 161 measurements completed at timepoint 1, and a total of 62 participants have completed every psychological and speech analysis measurement. Conclusions It appears that the tone and modulation of speech are as important, if not more so, than the content, and should not be underestimated. This is particularly evident in the interpretation of the psychological findings thus far acquired. Therefore, the application of a software analysis tool could increase the accuracy of finding assessments and improve patient care. Trial Registration ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008 International Registered Report Identifier (IRRID) PRR1-10.2196/13852

Conducta suicida y los parámetros acústicos de la voz y el habla. Revisión sistemática

“…Por su parte Mitra et al (2015) plantean en su estudio que las tareas de lectura y de libre expresión se comportan de manera diferente, coincidiendo con el resto de los autores en que la mejor tarea es la de libre expresión (Figueroa et al, 2020;Hashim et al, 2016;Jiang et al, 2017Jiang et al, , 2018Mitra et al, 2015;Ozdas et al, 2000;Parekh & Patil, 2017;Scherer et al, 2013Scherer et al, , 2015Subari et al, 2010;Suwannakhun & Yingthawornsuk, 2019;Tonn et al, 2020;Yingthawornsuk, 2016;Yingthawornsuk & Shiavi, 2008;Zhang et al, 2020). Esta información es de utilidad, ya que nos indica que en las evaluaciones debemos incluir, por lo menos, una tarea de libre expresión, lectura de texto y/o descripción de imágenes.…”

“…Está bien documentado que las emociones influyen en la voz. El comportamiento suicida se ha vinculado a la depresión y, por ende, algunos investigadores están tratando de encontrar signos tempranos para ayudar a detectar la depresión a través del análisis acústico de la voz de un paciente (Tonn et al, 2020).…”

“…En la época actual, gracias al surgimiento de nuevas tecnologías, se ha abierto la oportunidad a crear y desarrollar nuevas formas de evaluación del riesgo suicida. En este punto se ha descrito a la tecnología basada en la voz y el habla como una herramienta útil para identificar el riesgo suicida (Tonn et al, 2020). La creación de un dispositivo basado en los parámetros acústicos de la voz y el habla podría proporcionar una medida cuantitativa basada en la fisiología de los estados suicidas, añadiendo así una herramienta al arsenal clínico.…”

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

“…Nonetheless, smartphone-based monitoring of objective data in mood disorders is a rapidly growing approach and a highly innovative research field. This is evident in a number of study protocols stating ambitions to expand and intensify research in the field [ 64 , 65 ]. Although promising, a much larger evidence base is required to fully realize the potential, as well as the risks, of these approaches.…”

Development of a Speech-based Composite Score for Remotely Quantifying Language Changes in Frontotemporal Dementia

“…Recent studies have begun to use automated analysis tools to profile the specific speech and language changes in FTD subtypes in more detail ( Cho, Nevler, Ash, et al, 2021 ; Nevler et al, 2019 ; Nevler, Ash, McMillan, et al 2020 ) and have identified changes such as decreased speech output and lower lexical diversity, which are common across subtypes ( Cho, Nevler, Ash, et al, 2021 ). An ongoing study analyzing digital content-free speech data, language, and tone may provide equally important insights for diagnosis and care management ( Tonn et al, 2020 ).…”