Audio-Based PTSD Diagnosis Using Speech and Emotion Cues
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Solution Overview
Problem
Current PTSD diagnosis methods rely heavily on subjective clinician interpretation of patient verbal descriptions, leading to potential over- or under-diagnosis due to inaccurate reflection of mental state and non-verbal cues, and are time-consuming and costly when multiple clinicians are involved.
Innovation Solution
A computer-enabled method processes audio data to identify both verbal and non-verbal cues, converting audio to text, segmenting speakers, analyzing speech characteristics, and generating structured data for objective diagnosis, providing user interfaces for clinicians to analyze indicators systematically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple clinicians interview a patient to improve diagnostic accuracy, then diagnosis reliability is improved, but operational cost and time consumption increase significantly
Solution Approach 1:
An automated audio analysis system serves as an intermediary between the clinician and patient interview process. The system objectively analyzes audio recordings of patient interviews, extracting verbal and non-verbal cues to generate diagnostic indicators. This intermediary provides consistent, quantifiable measurements that support clinician decision-making without requiring multiple clinicians, thereby maintaining diagnosis reliability while improving operational efficiency
Solution Approach 2:
The patent replaces the mechanical system of multiple human clinicians performing subjective assessments with an automated audio analysis system using machine learning algorithms. The system processes audio data, extracts features, and generates diagnostic indicators objectively, substituting human interpretation variability with consistent algorithmic analysis. This substitution maintains or improves diagnostic reliability while significantly reducing time and cost requirements
2Device complexity
If clinicians rely on patient verbal descriptions for diagnosis, then diagnostic process is simple, but measurement precision deteriorates due to subjective interpretation and inaccurate patient reflection
Solution Approach 1:
The system merges multiple data sources including verbal descriptions, non-verbal audio cues (tone, pitch, speech rate, pauses), and structured diagnostic criteria into a unified analysis framework. By combining these diverse elements and processing them through machine learning models, the system achieves more precise and accurate diagnoses while maintaining manageable process complexity through automated integration
Solution Approach 2:
The patent transforms subjective diagnostic assessments into objective quantifiable parameters by analyzing audio signals. The system extracts measurable features such as speech rate, pitch variation, pause duration, and vocal intensity, converting qualitative clinical observations into quantitative data. This parameter transformation enables precise, consistent measurement of patient mental state without increasing diagnostic process complexity
Data Source
AI summary
The present disclosure generally relates to a system and method for obtaining a diagnosis of a mental health condition. An exemplary system can receive an audio input; convert the audio input into a text string; identify a speaker associated with the text string; based on at least a portion of the audio input, determine a predefined audio characteristic of a plurality of predefined audio characteristics; based on the determined audio characteristic, identify an emotion corresponding to the portion of the audio input; generate a set of structured data based on the text string, the speaker, the predefined audio characteristic, and the identified emotion; and provide an output for obtaining the diagnosis of the mental disorder or condition, wherein the output is indicative of at least a portion of the set of structured data.