Background Mood disorders and depression are pervasive and significant problems worldwide. These represent severe health and emotional impairments for individuals and a considerable economic and social burden. Therefore, fast and reliable diagnosis and appropriate treatment are of great importance. Verbal communication can clarify the speaker’s mental state—regardless of the content, via speech melody, intonation, and so on. In both everyday life and clinical conditions, a listener with appropriate previous knowledge or a trained specialist can grasp helpful knowledge about the speaker's psychological state. Using automated speech analysis for the assessment and tracking of patients with mental health issues 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 aim of this study is to evaluate the measurements of the presence or absence of depressive mood in participants by comparing the analysis of noncontentual speech parameters with the results of the Patient Health Questionnaire-9. Methods This proof-of-concept study included participants in different affective phases (with and without depression). The inclusion criteria included a neurological or psychiatric diagnosis made by a specialist and fluent use of the German language. The measuring instrument was the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters based on machine learning and the assessment of the findings using Patient Health Questionnaire-9. Results A total of 292 psychiatric and voice assessments were performed with 163 participants (males: n=47, 28.8%) aged 15 to 82 years. Of the 163 participants, 87 (53.3%) were not depressed at the time of assessment, and 88 (53.9%) participants had clinically mild to moderate depressive phases. Of the 163 participants, 98 (32.5%) showed subsyndromal symptoms, and 19 (11.7%) participants were severely depressed. In the speech analysis, a clear differentiation between the individual depressive levels, as seen in the Patient Health Questionnaire-9, was also shown, especially the clear differentiation between nondepressed and depressed participants. The study showed a Pearson correlation of 0.41 between clinical assessment and noncontentual speech analysis (P<.001). Conclusions The use of speech analysis shows a high level of accuracy, not only in terms of the general recognition of a clinically relevant depressive state in the participants. Instead, there is a high degree of agreement regarding the extent of depressive impairment with the assessment of experienced clinical practitioners. From our point of view, the application of the noncontentual analysis system in everyday clinical practice makes sense, especially with the idea of a quick and unproblematic assessment of the state of mind, which can even be carried out without personal contact. Trial Registration ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008

BACKGROUND Mental illnesses are a significant problem worldwide. Mood disorders and depression are pervasive. These represent severe health and emotional impairment for the individual and a considerable economic and social burden. On the one hand, fast and reliable diagnosis and appropriate treatment and care are therefore of great importance. The initial diagnosis and follow-up, especially in rural areas, must be carried out by physicians who do not have much psychiatric experience. Verbal communication can make the speaker’s mental state clear - regardless of the content, also via speech melody, intonation, etc. Both in everyday life and under clinical conditions, a listener with the appropriate previous knowledge or specialist training can grasp helpful knowledge about the speaker's psychological state. However, the presence of experienced therapists and the necessary time is often not available, leaving new opportunities to capture linguistic, noncontentual information. To improve the care of patients with depression, we have done a proof-of-concept study with a specialized tool for assessing their most critical cognitive parameters through a non-consensual analysis of their active speech. Using speech analysis for assessment and tracking mental health patients opens up the possibility of remote, automatic, and ongoing evaluation, when used with patients‘ smartphones, as part of the current trends towards the increasing use of digital and mobile health tools. OBJECTIVE The primary aim of this study is to evaluate the measurements of the presence or absence of a depressive mood of the participants by comparing the analysis of noncontentual speech parameters to the results of the Patient Health Questionnaire [PHQ]. METHODS Proof-of-concept study including participants in different affective phases (with and without depression). Inclusion criteria include a neurological or psychiatric diagnosis made by a specialist and fluent use of the German language. Exclusion criteria include diagnoses like 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 the findings using psychometric instruments and questionnaires (PHQ-9). RESULTS 292 psychiatric and voice assessments were done with 163 participants (47 males) aged 15-82 years. Eighty-seven participants were not depressed at assessment time, clinically mild to moderate depressive phases were identified in 88 participants at the assessment time. Ninety-eight participants showed subsyndromal Symptoms, but 19 participants were severely depressed. In the speech analysis, a clear differentiation between the individual depressive levels, as seen in the PHQ-9, was also shown, especially the clear differentiation between non-depressed and depressed participants. The study shows a Pearson correlation of 0.41 between clinical assessment and non-contentual speech analysis (p<0.0001). CONCLUSIONS The use of speech recognition shows a high level of accuracy, not only in terms of the general recognition of a clinically relevant depressive state in the subjects. Instead, there is also a high degree of agreement about the extent of the depressive impairment with the assessment of the experienced clinical practitioners. From our point of view, the application of the non-contentual analysis system in everyday clinical practice makes sense, especially with the idea of a quick and unproblematic assessment of the state of mind, which can even be carried out without contact CLINICALTRIAL Clinicaltrials.gov NCT03700008

UNSTRUCTURED Background Worldwide, the prevalence of mental disorders is very high and the guideline-oriented care of patients depends greatly on an early diagnosis as well as a regular and valid evaluation of the course, to be able to intervene quickly in case of imminent recurrence or deterioration in therapeutic terms. Experienced physicians and psychotherapists are neccessary for diagnostics and treatment but not available in sufficient numbers everywhere, especially in rural areas or in less well-developed countries. The human language is capable of revealing the psychic situation of the speaker by altering the non-contentual aspects (speech melody, intonations, speech rate, etc.). The time and experience to learn the speechpatterns of a patient in healthy and ill moments is often unavailable, leaving the opportunities inherent in capturing linguistic, non-contentual information unused. In order to improve the care of patients with mental disorders just under these aspects, we have developed a concept for assessing the most important mental parameters through a non-contentual analysis of the active speech. Using speech analysis for assessment and tracking of mental health patients, opens also the invaluable possibilities of remote, automatic and ongoing evaluation, when used with patients‘ smartphones, as part of the strong digital and mobile health trends. Methods/Design In this paper, we describe a two-arm, randomized controlled trial. The participants are all recruited in one outpatient neuropsychiatric treatment center. Inclusion criteria are e.g. a psychiatric diagnosis made by a specialist, no terminal or life-threatening illness, fluent use of the German language, exclusion criteria are e.g. psychosis, dementia, speech or language disorders, addiction history or suicide attempt currently or in the last 12 months. The measuring instruments are the "VoiceSense" voice analysis tool, which enables the analysis of 200 specific speech parameters and assessment of the findings through the use of psychometric questionnaires. Discussion The importance of content-free speech patterns should not be overestimated. This is particularly evident in the interpretation of the psychological findings. Applying a software analysis tool can increase the accuracy of finding assignments and improve patient care. Trial Registration This study is registered at „clinicaltrials.gov“, Number was NCT03700008, registration date 09 October 2018, http://www.clinicaltrials.gov.