Speech Fluency Evaluation Tool with Real-Time ML Feedback
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Solution Overview
Problem
Current technologies lack a real-time, accurate method for evaluating speech fluency and providing feedback, particularly in real-world environments, which is crucial for conditions like stuttering and public speaking performance.
Innovation Solution
A speech fluency evaluation and feedback tool that utilizes a computing device to collect and analyze speech data, applying machine learning to detect speech events and generate fluency scores, providing real-time feedback through a user device or post-processing analysis, with speech pattern models trained using various datasets and algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time speech analysis is implemented, then speech fluency evaluation accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-processing speech data and preparing analysis frameworks before actual fluency evaluation. Speech data is pre-processed to extract relevant features, and analysis models are pre-configured, which reduces the computational burden during real-time evaluation while maintaining accuracy.
Solution Approach 2:
The patent replaces complex mechanical speech analysis systems with electronic and software-based solutions. Machine learning models and automated algorithms substitute for manual or computationally intensive traditional speech analysis methods, enabling real-time processing with reduced resource consumption.
2Measurement precision
If comprehensive speech data collection is performed, then analysis accuracy is improved, but data processing time increases
Solution Approach 1:
The system extracts only the most relevant features and data points from comprehensive speech data collections. By identifying and extracting key speech characteristics necessary for fluency evaluation, the system maintains high analysis accuracy while significantly reducing the volume of data requiring processing.
Solution Approach 2:
The speech data processing is divided into multiple segments or stages. Different aspects of speech analysis are processed separately and independently, allowing parallel processing and optimization of each segment. This segmentation reduces overall processing time while maintaining comprehensive analysis accuracy.
3Adaptability or versatility
If real-time feedback is provided, then user benefit is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where speech evaluation results are immediately returned to users in real-time. This feedback loop enables users to receive instant guidance and adjustments, improving adaptability and user benefit while the automated nature of the feedback reduces the need for complex manual intervention systems.
Solution Approach 2:
The system provides self-service capabilities where users can independently interact with the speech analysis tool, receive evaluations, and obtain feedback without requiring complex administrative or technical infrastructure. The automated processing and user-friendly interface reduce system complexity from the user perspective.
Data Source
AI summary
Speech fluency evaluation and feedback tools are described. A computing device such as a smartphone may be used to collect speech (and/or other data). The collected data may be analyzed to detect various speech events (e.g., stuttering) and feedback may be generated and provided based on the detected speech events. The collected data may be used to generate a fluency score or other performance metric associated with speech. Collected data may be provided to a practitioner such as a speech therapist or physician for improved analysis and/or treatment.