AI Discourse Feedback Engine for Real-Time Speech Coaching
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Solution Overview
Problem
Conventional techniques for developing speech and debate skills lack immediate, personalized qualitative feedback, hindering the refinement of communication and debate abilities due to reliance on self-assessment and absence of human judgment.
Innovation Solution
A discourse engine provides qualitative feedback on forensic activities like speech and debate exercises, generating insights into effectiveness, clarity, and audience impact through interaction with users and content analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-assessment is used for evaluating speech and debate performance, then the learning process can be initiated without external resources, but the feedback is insufficient and lacks detailed insights into areas for improvement
Solution Approach 1:
The system enables self-service by allowing users to practice speech and debate alone with the AI coach providing automated qualitative feedback. The discourse engine analyzes user performance and generates personalized feedback reports without requiring human coaches or judges, making the system accessible to anyone with a device.
Solution Approach 2:
The patent implements feedback by having the discourse engine continuously monitor user performance during speech and debate exercises, compare it against established criteria, and generate detailed qualitative feedback. This feedback loop provides specific insights into areas for improvement in clarity, persuasiveness, and engagement techniques.
2Loss of information
If human coaches or judges are used to provide qualitative feedback, then detailed and constructive evaluations can be obtained, but the system becomes complex and resource-intensive
Solution Approach 1:
The patent replaces the mechanical system of human coaches and judges with an AI-based discourse engine. The system uses natural language processing and machine learning models to analyze user performance and generate qualitative feedback, eliminating the need for human evaluators while maintaining detailed and constructive evaluations.
Solution Approach 2:
The discourse engine acts as an intermediary between the user and the evaluation process. It mediates by analyzing user performance data, comparing it against established speech and debate criteria, and generating personalized feedback reports, thereby simplifying the system while maintaining high-quality feedback.
3Productivity
If immediate feedback is provided during practice, then the learning process is accelerated, but the system requires sophisticated real-time analysis capabilities
Solution Approach 1:
The system provides continuous feedback during the user's speech or debate by continuously monitoring performance in real-time. The discourse engine processes audio input, analyzes delivery and content, and provides immediate feedback on clarity, persuasiveness, and engagement techniques, accelerating the learning process through uninterrupted feedback loops.
Solution Approach 2:
The patent uses AI-based natural language processing and machine learning models to enable real-time analysis of user performance. These sophisticated algorithms process audio input and generate immediate feedback without human intervention, providing continuous and accelerated learning feedback.
Data Source
AI summary
Systems and methods for a discourse engine for providing qualitative feedback are described herein. In an example, a discourse engine may determine a speech type for a speech exercise selected by a client device. The discourse engine may determine one or more qualitative categories for speech feedback and determine one or more qualitative aspects per the one or more qualitative categories for the speech feedback. The discourse engine may receive first speech content from the client device and generate first speech feedback based on the one or more qualitative aspects and the first speech content. The discourse engine may then provide the first speech feedback to the client device.