Articulatory Event Weighting for Speech-Based Disease Evaluation
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Solution Overview
Problem
Existing systems face challenges in accurately evaluating the physiological state of a subject based on speech analysis, particularly in conditions like Parkinson's disease, due to the difficulty in differentiating between speech units that are indicative of the condition.
Innovation Solution
A system that computes discrimination-effectiveness scores by analyzing articulatory event-types, such as phonemes, using same-state and cross-state distances, neuronal outputs, or speech models to determine the state of a subject, and can adapt to different languages through neural network tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech analysis is performed using conventional methods, then the evaluation process is simple, but the accuracy of evaluating physiological states is insufficient
Solution Approach 1:
The speech signal is segmented into discrete articulatory event-types (phonemes, diphones, syllables) rather than analyzing continuous speech. This segmentation enables precise identification of specific speech units that are indicative of physiological conditions, thereby improving measurement precision while managing system complexity through focused analysis of discrete elements.
Solution Approach 2:
Different speech units (articulatory event-types) are assigned different weights based on their discriminatory power for detecting specific physiological conditions. This local quality approach allows the system to focus computational resources on the most informative speech units, improving evaluation accuracy without uniformly increasing complexity across all speech features.
2Measurement precision
If all speech units are analyzed equally, then the analysis process is simple, but the ability to differentiate indicative speech units is reduced
Solution Approach 1:
The system changes the parameter of speech unit weighting by computing discrimination-effectiveness scores that quantify how well each articulatory event-type indicates a particular physiological state. Speech units are then weighted according to these scores, transforming the analysis from uniform treatment to differentiated weighting based on empirical performance metrics.
Solution Approach 2:
The manual or uniform analysis of speech units is replaced with an automated computational system that calculates discrimination-effectiveness scores using machine learning models. This substitution enables the system to automatically identify and weight indicative speech units without requiring manual intervention, improving differentiation capability while managing complexity through automation.
3Adaptability or versatility
If speech analysis is performed without language adaptation, then the system is simpler, but the applicability to different languages is limited
Solution Approach 1:
The system achieves language versatility through a universal framework that processes multiple languages using the same underlying methodology. The discrimination-effectiveness scoring and weighting mechanisms are language-agnostic, applying uniformly across different languages while adapting to language-specific phonetic characteristics, thereby enabling multi-language support without requiring separate analysis systems for each language.
Data Source
AI summary
A method includes obtaining, by a processor, a score quantifying an estimated degree to which an instance of an articulatory event-type indicates a state, with respect to a disease, in which the instance was produced. The method further includes, using the score, facilitating, by the processor, a computer-implemented procedure for evaluating the state of a subject based on a test utterance produced by the subject. Other embodiments are also described.