Speech Practice Media Synchronization Using Predicted Speech Patterns
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Solution Overview
Problem
Speech-impaired individuals face difficulties in speaking and training to improve their condition, and existing methods lack effective integration with media content for enhanced learning.
Innovation Solution
A system and method for Speech Practice with Media Content Synchronization, utilizing a Speech Detection Component to analyze speech metrics, compute deviation metrics, train a machine learning model to predict speech patterns, and transform reference speech based on these patterns using Generative Adversarial Networks (GAN) algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional speech therapy methods are used, then speech training can be provided, but the integration with media content for enhanced learning is insufficient
Solution Approach 1:
The patent combines speech therapy training with media content playback by synchronizing the two. The speech impairment training system merges the therapeutic exercise component with the media consumption component, allowing users to practice speech while watching videos or listening to audio content. This integration ensures that media content serves dual purposes: entertainment/education and speech therapy reinforcement.
Solution Approach 2:
The system makes media content serve multiple functions simultaneously. The same media content is used both for its original purpose (entertainment, education, information) and for speech therapy reinforcement. The speech impairment training system allows a single media player to function as both a standard media player and a therapeutic training tool, maximizing the utility of available content.
2Measurement precision
If speech metrics are detected and analyzed in real-time, then personalized feedback can be provided, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary speech analysis layer that sits between the user's speech input and the feedback mechanism. This intermediary component automatically detects speech metrics such as articulation clarity, pronunciation accuracy, and speech patterns, then translates these measurements into actionable feedback. The intermediary handles the complexity of real-time audio analysis, preventing it from overwhelming the overall system architecture.
Solution Approach 2:
The system incorporates automated speech analysis and feedback generation that operates without requiring manual therapist intervention for every measurement. The speech impairment training system automatically detects speech metrics, compares them against target patterns, and provides real-time feedback, allowing the system to serve itself in the analysis and evaluation functions.
3Adaptability or versatility
If media content is transformed based on predicted speech patterns, then personalized training is improved, but the processing time increases
Solution Approach 1:
The patent employs preliminary action by pre-processing and pre-analyzing speech patterns from the user before media content playback begins. The speech impairment training system captures baseline speech metrics and predicts speech patterns in advance, allowing the media content to be pre-synchronized with expected speech timing. This preliminary analysis reduces real-time processing requirements during actual training sessions.
Solution Approach 2:
The system dynamically adjusts media content transformation based on real-time speech pattern detection. Rather than applying fixed transformations, the speech impairment training system continuously monitors speech metrics and adapts the synchronization and transformation parameters on the fly, optimizing processing efficiency while maintaining personalized training effectiveness.
Data Source
AI summary
An embodiment includes detecting by a Speech Detection Component of a system a speech metric of a speaker in response to a reference speech. The embodiment includes responsive to the detected speech metric, computing by a Speech Analysis Component of the system a deviation metric between the speech metric and the reference speech. The embodiment includes training a machine learning model by a Speech Prediction Component of the system based on the deviation metric to generate a predicted speech pattern of the speaker. The embodiment also includes transforming by a Controller Component of the system the reference speech based on the predicted speech pattern.