Speech Model Mapping for Physiological State Detection
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Solution Overview
Problem
Current medical diagnostic systems for physiological conditions affecting speech lack effective methods to accurately assess changes in speech patterns over time, particularly in identifying onset or deterioration of conditions like congestive heart failure, COPD, or depression, using speech analysis.
Innovation Solution
A method and system that construct speech models from reference speech samples with known physiological states, using local distance functions and allowed transitions to map test speech samples to minimum-distance sequences, generating outputs indicating the subject's physiological state by comparing total distances to thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speech analysis is used to monitor physiological conditions, then diagnostic capability is improved, but accuracy in identifying changes over time deteriorates
Solution Approach 1:
The system performs preliminary action by constructing speech models from reference speech samples collected when the subject's physiological state is known to be stable. These pre-established models serve as baselines for future comparison, enabling the system to detect deviations from normal speech patterns that indicate physiological changes or deteriorations.
Solution Approach 2:
The system applies parameter changes by computing multiple different distance measures between test speech samples and reference models, each distance measure evaluating different acoustic parameters. By minimizing a combined total distance across multiple parameter dimensions, the system achieves more precise detection of physiological state changes than single-parameter analysis would provide.
2Measurement precision
If multiple distance measures are computed to improve accuracy, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The system segments the complex comparison task into multiple independent distance measure computations, each evaluating specific acoustic parameters separately. By dividing the overall distance computation into manageable segments (different distance measures), the system maintains measurement precision while organizing computational complexity into structured, manageable components that can be processed systematically.
Data Source
AI summary
A method includes obtaining one or more speech models, each model including one or more acoustic states and, provided that the model includes multiple acoustic states, allowed transitions therebetween. The method further includes receiving a speech sample produced by a subject while a physiological state of the subject was unknown. The method further includes mapping at least one sample portion of the speech sample to a respective one of the speech models, by computing a plurality of feature vectors quantifying acoustic features of different respective portions of the sample portion, and mapping the feature vectors to respective acoustic states included in the speech model such that a total distance between the feature vectors and the respective acoustic states is minimized. The method further includes, in response to mapping the sample portion to the speech model, communicating an output indicating the physiological state of the subject. Other embodiments are also described.