Speech Analysis System for Objective Scoring and Biofeedback
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Solution Overview
Problem
Current methods for diagnosing and treating speech and language pathologies lack efficient and controlled practice experiences outside clinical settings, with limited objective scoring and feedback for patients and speech-language pathologists.
Innovation Solution
A system and method that provide a fully instrumented practice experience using Speech Quality and language metrics, generating real-time biofeedback, which includes processing vocal responses to measure linguistic and acoustic parameters, transforming them into text, comparing to expected responses, and computing objective quality scores, utilizing natural language processing and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech and language therapy practice occurs in a one-on-one clinical setting without control mechanisms, then the SLP can provide personalized attention, but the SLP loses control over the frequency, duration, content and quality of practice sessions outside the clinic
Solution Approach 1:
The system enables patients to independently complete practice sessions using the digital platform, with automated tracking and monitoring of practice frequency, duration, and content. The SLP receives automated reports without needing to directly supervise each session, thus maintaining control while reducing operational complexity.
Solution Approach 2:
The system implements automated feedback loops where patient practice data is continuously collected, analyzed, and reported back to the SLP. This provides real-time or near-real-time information about practice quality and progress, enabling the SLP to maintain control over treatment effectiveness without direct involvement in each practice session.
2Measurement precision
If traditional clinical assessment methods are used, then the SLP can evaluate speech and language quality through direct observation, but objective scoring and quantitative metrics are limited
Solution Approach 1:
The system replaces manual clinical assessment with automated voice analysis technology. Speech and language samples are processed through algorithms that objectively measure acoustic parameters, pronunciation accuracy, and language usage patterns, providing precise quantitative metrics without requiring complex manual evaluation procedures.
Solution Approach 2:
The system introduces an intermediary processing layer between the patient's speech production and the SLP's evaluation. This intermediary automatically transcribes, analyzes, and scores speech samples using natural language processing and acoustic analysis, providing objective measurements that bridge the gap between patient performance and clinical assessment.
3Productivity
If real-time biofeedback is provided during practice sessions, then patient engagement and immediate correction are improved, but processing and computation resources increase
Solution Approach 1:
The system provides real-time feedback on the most critical speech and language parameters while deferring analysis of less critical aspects to post-session processing. This selective real-time analysis reduces computational demands during practice sessions while maintaining effectiveness by addressing the most important corrective needs immediately.
Solution Approach 2:
The feedback process is divided into multiple stages: immediate real-time feedback on basic pronunciation and speech production, followed by more comprehensive linguistic and semantic analysis after the session. This segmentation allows real-time feedback to be provided with minimal computational overhead while still delivering thorough assessment results.
Data Source
AI summary
There is provided herein a method for assessing a speech/lingual quality of a subject, the method comprising: providing a content-containing stimulus to a user; recording the user's vocal response to the stimulus and/or to instructions related thereto; processing the user's recorded vocal response to measure/extract/compute at least one linguistics (prosodic) parameter and at least one acoustic parameter; transforming the user's vocal response into a transformed text section, which is based on a processing unit's interpretation of the user's verbal response; comparing the transformed text section to a predetermined text section, which represents the user's expected; and computing an output signal indicative of at least one speech/lingual quality of the user, based at least on data resulted from the texts comparison, the at least one measured/extracted/computed linguistic parameter and the at least one acoustic parameter.