Vocal Feature Extraction for Real-Time Mental State Assessment
Solution Overview
Problem
Conventional methods for diagnosing mental health disorders, such as depression and suicide risk, are inaccurate and unreliable due to reliance on patient self-reporting and lack of real-time assessment, leading to under-diagnosis and under-treatment, especially in crisis situations like suicide hotlines where counselors may not be mental health professionals.
Innovation Solution
A system and method for extracting vocal features from voice signals to determine emotional or mental states, using techniques like preprocessing, feature extraction, and prediction processing, which includes acquiring audio signals, extracting signal components, and applying models like linear classifiers or Neural Networks to provide real-time feedback on suicidal risk and counselor apprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnosis methods relying on patient self-reporting are used, then the diagnostic process is simple to implement, but the accuracy and reliability of diagnosis deteriorates
Solution Approach 1:
The patent replaces the mechanical/self-reporting based diagnosis system with an automated audio signal processing system. The system uses computational algorithms to analyze vocal features (pitch, tone, rhythm) and automatically assess mental state, eliminating the need for manual self-reporting while improving diagnostic accuracy through objective measurement of paralinguistic content.
Solution Approach 2:
The patent introduces an intermediary audio analysis system that acts as a mediator between the patient's speech and the diagnosis. This intermediary processes the audio signals, extracts relevant features, and provides objective assessments, thereby improving the reliability of diagnosis without requiring direct self-reporting from the patient.
2Speed
If real-time audio analysis is implemented, then the speed of assessment improves, but the computational resources and processing complexity increase
Solution Approach 1:
The patent segments the audio signal processing into distinct stages: pre-processing (noise reduction, voice activity detection), feature extraction (pitch, tone, rhythm analysis), and classification. This segmentation allows real-time processing by handling complex computations in manageable steps, improving assessment speed while optimizing energy consumption through efficient processing pipelines.
Solution Approach 2:
The patent applies partial action by focusing computational resources on extracting only the most relevant vocal features (pitch, tone, rhythm) rather than analyzing all possible audio characteristics. This selective feature extraction enables real-time assessment by reducing the computational burden while maintaining sufficient accuracy for mental state evaluation.
3Measurement precision
If multiple vocal features are extracted and analyzed, then the measurement precision of emotional state improves, but the device complexity and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-defining the specific vocal features to be extracted (pitch, tone, rhythm) based on established research about mental state indicators. This pre-planned feature selection avoids time-consuming exploratory analysis and enables efficient real-time processing while maintaining high measurement precision through targeted feature extraction.
Solution Approach 2:
The patent applies local quality by analyzing specific segments of the audio signal (individual vocal features) rather than treating the entire speech signal uniformly. This allows the system to focus computational effort on the most informative local characteristics (pitch contours, tone variations, rhythm patterns) improving detection accuracy while reducing overall processing time through selective analysis.
Data Source
AI summary
Embodiments of the present systems and methods may provide techniques for extracting vocal features from voice signals to determine an emotional or mental state of one or more persons, such as to determine a risk of suicide and other mental health issues. For example, as a person's mental state may indirectly alters his or her speech, suicidal risk in, for example, hotline calls, may be determined through speech analysis. In embodiments, such techniques may include preprocessing of the original recording, vocal feature extraction, and prediction processing. For example, in an embodiment, a computer-implemented method of determining an emotional or mental state of a person, the method comprising acquiring an audio signal relating to a conversation including the person, extracting signal components relating to an emotional or mental state of at least the person, and outputting information characterizing the extracted emotional or mental state of the person.