Audio Analysis System for Autism Detection via Vocalization Metrics
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Solution Overview
Problem
Current methods for assessing a child's expressive language development are invasive, costly, and time-consuming, requiring human observers and labor-intensive audio transcription, which limits the ability to provide timely and cost-effective metrics for improving language skills and detecting developmental disorders like autism.
Innovation Solution
A system and method that uses a recorder and processor-based device to capture and analyze audio recordings from a child's language environment, segmenting audio into key child and adult segments, and applying age-based models to estimate metrics such as vocalization frequency and conversational turns, independent of content, to assess language development and detect disorders like autism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observers and manual transcription are used to assess language development, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual human observation and transcription with automated acoustic analysis systems that use algorithms to detect and measure child vocalizations, converting speech analysis from a manual mechanical process to an automated computational one
Solution Approach 2:
The patent introduces audio recording devices and computational algorithms as intermediaries between the child's natural speech and the assessment metrics, allowing indirect measurement that preserves ecological validity while enabling automated analysis
2Measurement precision
If human observers are used to collect language data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent enables the assessment system to automatically perform data collection, segmentation, and analysis without requiring human observers, with the system serving itself through automated algorithms that process recordings and generate metrics
Solution Approach 2:
The patent extracts the essential measurement function from complex human observation processes, isolating the core task of detecting vocalization metrics into simplified automated algorithms that capture key language development indicators
3Measurement precision
If content-based analysis is performed on audio recordings, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent extracts and measures specific acoustic features and vocalization patterns from audio recordings without requiring full transcription or content analysis, obtaining language development metrics directly from acoustic properties
Solution Approach 2:
The patent performs partial analysis by focusing only on the essential acoustic features needed for language assessment rather than analyzing complete speech content, achieving sufficient measurement precision with reduced processing effort
Data Source
AI summary
In one embodiment, a method for detecting autism in a natural language environment using a microphone, sound recorder, and a computer programmed with software for the specialized purpose of processing recordings captured by the microphone and sound recorder combination, the computer programmed to execute the method, includes segmenting an audio signal captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality recording segments. The method further includes determining which of the plurality of recording segments correspond to a key child. The method further includes determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings. Additionally, the method includes extracting phone-based features of the key child recordings; comparing the phone-based features of the key child recordings to known phone-based features for children; and determining a likelihood of autism based on the comparing.