Speech Analysis Models for Non-Disruptive Mental Health Scoring
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Solution Overview
Problem
Existing psychiatric assessments for depression and anxiety disorders rely heavily on patient collaboration through questionnaires, which can be incomplete or deceptive, limiting the validity of mental health scoring.
Innovation Solution
Analyze speech patterns using machine-learning models to predict mental health scores by extracting audio and textual features from discussions, including audio bursts and n-grams, to provide an automatic and non-disruptive assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If questionnaire-based assessment is used, then patient collaboration is obtained, but reliability of mental health scoring deteriorates due to incomplete or deceptive responses
Solution Approach 1:
The patent introduces speech as an intermediary medium between the patient and the assessment system. Instead of directly asking patients to self-report their mental state through questionnaires, the system analyzes speech patterns as an indirect indicator, thereby obtaining reliable assessment data without requiring direct patient collaboration or self-disclosure.
Solution Approach 2:
The patent replaces the mechanical questionnaire-filling process with an automated speech analysis system using machine learning models. This substitution eliminates the need for patients to manually complete questionnaires and provides continuous, objective assessment based on speech characteristics, thereby improving reliability while reducing operational burden on patients.
2Measurement precision
If questionnaire completion is required, then mental health assessment is obtained, but patient collaboration deteriorates when mental condition worsens
Solution Approach 1:
The system processes speech data that is naturally produced during therapy sessions without requiring patients to perform additional actions. The speech analysis operates automatically on existing communication data, making the assessment process self-service in nature and eliminating barriers related to patient fatigue or lack of motivation.
Solution Approach 2:
The speech analysis provides continuous assessment throughout therapy sessions rather than relying on discrete questionnaire completions. This continuous monitoring of speech patterns ensures that mental health evaluation occurs consistently regardless of patient condition, maintaining measurement precision without interrupting the therapeutic flow.
3Reliability
If automatic speech analysis is implemented, then reliability of assessment is improved, but device complexity increases
Solution Approach 1:
The speech analysis system serves multiple functions: it assesses mental health status, monitors patient progress over time, and provides objective data for clinical decision-making. By consolidating these functions into a single automated platform, the system achieves high reliability without proportionally increasing complexity, as the same infrastructure supports diverse assessment needs.
Solution Approach 2:
The system transforms complex speech signals into simplified quantitative parameters through machine learning feature extraction. By converting raw audio data into standardized speech features and metrics, the system maintains high assessment reliability while managing computational complexity through parameter transformation and dimensionality reduction.
Data Source
AI summary
Systems and methods of the present invention may relate to prediction of a mental health score, representing a mental condition of a speaker. Embodiments of the invention may analyze speech by receiving an audio data element representing a discussion; extracting a first set of audio segments pertaining to speech of a first speaker in the discussion; analyzing the first set of audio segments to produce a set of audio features; and applying a machine-learning (ML) model on the set of audio features, to predict a mental health score, representing a mental condition of the first speaker. Embodiments of the invention may provide technical means for supporting psychiatric assessment of mental disorders such as depression and anxiety in an automatic, nondisruptive manner, e.g., without direct patient's collaboration, thereby increasing validity of the assessment.