Speech Analysis System for Interpretable Mental Health Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in effectively assessing and addressing the social and functional impairments associated with severe mental illnesses like schizophrenia and bipolar disorder, particularly in using speech and language analysis for clinical practice, with a lack of objective and interpretable biomarkers for early diagnosis and prognosis.
Innovation Solution
Development of systems and methods that analyze speech and audio data to identify elemental components of language and acoustics, using machine learning models to evaluate social and functional competency, and provide interpretable metrics for clinical assessments, including the use of language features such as volition, affect, lexical diversity, and syntactic complexity, to predict mental health status and social participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated computational models are used to assess mental illness using speech and language features, then assessment efficiency and objectivity are improved, but interpretability and clinical applicability worsen
Solution Approach 1:
The patent segments the assessment into distinct modules: speech feature extraction (acoustic, linguistic, paralinguistic), machine learning classification, and clinical interpretation. This segmentation allows each component to be optimized independently while maintaining overall system interpretability through modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer of clinically validated speech features that bridges the gap between raw audio data and clinical diagnoses. These features serve as interpretable intermediaries that translate complex computational models into clinically meaningful metrics, enhancing trust and applicability.
2Measurement precision
If comprehensive speech and language analysis is performed, then assessment accuracy is improved, but data processing time and computational resources worsen
Solution Approach 1:
The patent applies partial action by selectively extracting and analyzing only the most relevant speech features (acoustic, linguistic, paralinguistic) rather than processing all possible audio data. This targeted approach maintains high assessment accuracy while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The patent changes parameters by transforming raw audio signals into standardized speech feature representations that can be efficiently processed by machine learning models. This parameter transformation enables accurate assessment with reduced computational complexity and faster processing speeds.
3Extent of automation
If existing speech analysis tools are used, then technical capability is improved, but clinical validation and reliability worsen
Solution Approach 1:
The patent implements feedback mechanisms where clinical experts validate and refine the speech feature extraction and classification processes. This iterative feedback loop ensures that the automated system continuously improves its reliability and alignment with clinical standards while maintaining advanced technical capabilities.
Solution Approach 2:
The patent applies preliminary action by pre-validating speech features against established clinical criteria before they are used in assessment. This preliminary validation ensures that only clinically relevant and reliable features are incorporated into the automated assessment system, building trust and reliability from the outset.
Data Source
AI summary
Disclosed herein are platforms, systems, software, and methods for evaluating social behavior. Speech or audio data can be analyzed to identify elemental language and acoustic components of speech that are used to determine higher order effects such as social behavior. Disclosed herein are models developed to address the assessment of mental health status (e.g. diagnosis and assessment of neurocognition and symptom ratings). In some embodiments, disclosed herein are models configured to predict performance on social and functional competency assessments. The present disclosure demonstrates the ability of a set of language features to provide several relevant upstream and/or downstream clinical assessments on audio derived data such as transcripts that were never seen during model training and showed consistent performance on all tasks of interest.