Articulation Disorder Detection Using Dual Window Voice Analysis
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Solution Overview
Problem
Existing technologies are inadequate in quickly detecting articulation disorders, which can hinder effective treatment and symptom alleviation.
Innovation Solution
An articulation disorder detection apparatus and method that generates first and second lines from averaged voice data of a test subject vocalizing a voice module, using specific window lengths to identify sections where the first line exceeds a threshold value, and employs a machine learning model to analyze spectrogram images for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional voice data analysis methods are used to detect articulation disorders, then detection accuracy can be maintained, but detection speed is slow and treatment intervention is delayed
Solution Approach 1:
The patent segments the voice data analysis into two distinct lines: a first line using short window length (≤ standard vocalizing time) to capture rapid vocalization patterns, and a second line using long window length (≥ standard time and ≤ twice the standard time) to capture overall rhythm. This segmentation allows the system to quickly identify articulation disorders by comparing patterns between the two lines, enabling rapid detection while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-establishing the relationship between short window length analysis and rapid vocalization patterns, and long window length analysis and overall rhythm. This preliminary framework allows the section detector to quickly compare the two lines and identify articulation disorders without requiring complex real-time analysis, thus speeding up detection while maintaining reliability.
2Measurement precision
If multiple analysis methods are combined to improve detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts the essential features of voice data into two distinct lines with specific window length characteristics. The first line extracts rapid vocalization patterns using short window length, while the second line extracts overall rhythm using long window length. By taking out only these essential features for comparison, the system achieves high detection accuracy without incorporating unnecessary complex analysis methods.
Solution Approach 2:
The patent creates a universal detection framework where the same section detector can identify articulation disorders by comparing the first and second lines. This multi-functional approach allows a single detection mechanism to handle various articulation disorder types by analyzing different vocalization patterns, improving accuracy without proportionally increasing complexity.
Data Source
AI summary
This articulation disorder detection device comprises: a section detection unit for detecting a section in which the value of a first line obtained by averaging, using a first window length, speech sound data obtained by having a subject to repeatedly utter a speech sound module is greater than the value obtained by multiplying, by a positive real number, the value of a second line obtained by averaging, using a second window length, the speech sound data; and a determination unit for determining an articulation disorder on the basis of the detection result of the section detection unit.