Speech-Based PTSD Assessment Using Acoustic Feature Extraction
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Solution Overview
Problem
Current methods for mental health monitoring, particularly for conditions like depression and PTSD, face challenges due to individual differences and the need for invasive and cumbersome data collection processes, which obscure inter-speaker effects and are not practical for longitudinal monitoring.
Innovation Solution
A speech-based system using mobile electronic devices to collect and analyze speech data through automated programs, extracting non-lexical features like prosodic and acoustic parameters to assess PTSD status, employing machine learning algorithms for accurate mental health assessments without direct probing of mental states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinician evaluations are used for mental health monitoring, then subjective assessment can be obtained, but individual differences and invasive data collection processes obscure inter-speaker effects and reduce measurement precision
Solution Approach 1:
The patent replaces the mechanical/invasive data collection system with an acoustic sensing system. Instead of using clinicians to directly assess patients through invasive questioning and observation, the system uses automated speech analysis to extract acoustic features (pitch, energy, spectral characteristics) that objectively indicate mental health status. This substitution eliminates the complexity of invasive data collection while improving measurement precision through automated, consistent acoustic measurements.
Solution Approach 2:
The patent introduces speech acoustic features as an intermediary between the patient's mental state and the assessment system. Rather than directly measuring mental health parameters through invasive clinical evaluation, the system uses speech characteristics (which naturally reflect emotional and psychological state) as a mediator. This intermediary approach allows indirect but precise measurement of mental health status without requiring invasive data collection procedures.
2Loss of information
If invasive data collection methods are used, then detailed mental health information can be obtained, but the process becomes cumbersome and impractical for longitudinal monitoring
Solution Approach 1:
The patent implements a self-service assessment system where patients provide speech samples through mobile devices without requiring clinician intervention for data collection. The automated program elicits speech, extracts acoustic features, and generates assessments independently. This self-service approach maintains information completeness by capturing relevant speech characteristics while dramatically improving ease of operation, enabling frequent longitudinal monitoring without the burden of invasive clinical procedures.
Solution Approach 2:
The patent replaces the mechanical process of invasive clinical data collection with an automated acoustic analysis system. Instead of requiring clinicians to conduct detailed assessments through direct patient interaction, the system uses automated speech processing to extract and analyze acoustic features. This substitution preserves the richness of mental health information while eliminating the operational complexity and invasiveness of traditional methods, making longitudinal monitoring practical and convenient.
3Ease of operation
If automated speech analysis is used, then non-invasive and cost-effective monitoring is achieved, but individual differences may obscure inter-speaker effects
Solution Approach 1:
The patent applies local quality by analyzing speech characteristics at multiple levels: individual speaker-specific features and population-level patterns. The system extracts acoustic features (pitch, energy, spectral characteristics) that capture both speaker-specific vocal qualities and universal indicators of mental health status. This multi-level analysis allows the system to maintain accessibility through automated non-invasive monitoring while preserving the ability to detect inter-speaker effects by distinguishing individual vocal characteristics from general mental health indicators.
Solution Approach 2:
The patent uses parameter changes by transforming raw speech signals into standardized acoustic feature parameters (pitch contours, energy distribution, spectral characteristics). These transformed parameters normalize individual differences while preserving mental health-related variations. By converting diverse speech data into consistent acoustic parameters, the system maintains ease of operation through automated analysis while improving measurement precision in detecting inter-speaker effects associated with specific mental health conditions.
Data Source
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AI summary
A computer-implemented method can include a speech collection module collecting a speech pattern from a patient, a speech feature computation module computing at least one speech feature from the collected speech pattern, a mental health determination module determining a state-of-mind of the patient based at least in part on the at least one computed speech feature, and an output module providing an indication of a diagnosis with regard to a possibility that the patient is suffering from a certain condition such as depression or Post- Traumatic Stress Disorder (PTSD).