Voice Recognition Calibration via Automatic Speech Pattern Recording
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Solution Overview
Problem
Existing voice recognition systems require extensive and tedious training sessions, are costly, and are not user-friendly, limiting their adoption in mainstream applications due to restrictive requirements such as speaking slowly and distinctly with pauses between words.
Innovation Solution
A method that records sounds associated with specific text, identifies word locations, and calibrates input streams using synchronization keywords to reduce training intensity and improve performance without the need for keystrokes, mouse clicks, or playback, allowing for faster and more accurate voice recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional voice recognition systems are used, then recognition capability is achieved, but training time and complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically recording and analyzing user speech patterns during normal computer usage without requiring dedicated training sessions. The calibration data is collected in advance during typical user interactions, eliminating the need for separate training time while maintaining recognition accuracy.
Solution Approach 2:
The voice recognition system calibrates itself automatically by monitoring and analyzing user speech patterns during normal operation. The system serves itself by collecting calibration data from everyday usage without requiring manual intervention or dedicated training sessions, thus reducing time loss while maintaining recognition reliability.
2Reliability
If traditional voice recognition systems are used, then speech recognition is achieved, but ease of operation deteriorates due to restrictive requirements
Solution Approach 1:
Instead of requiring users to conform to restrictive speaking patterns (slow, distinct, paused), the system inverts the approach by adapting to the user's natural speech patterns. The calibration process captures how the user actually speaks and adjusts recognition parameters accordingly, maintaining accuracy while greatly improving ease of operation.
Solution Approach 2:
The system dynamically adapts to individual user speech characteristics by continuously calibrating during normal usage. Rather than enforcing static, restrictive guidelines, the system adjusts its recognition parameters dynamically based on observed user behavior, making operation easier while preserving recognition accuracy.
3Measurement precision
If sophisticated voice recognition technology is implemented, then recognition performance is improved, but device complexity and cost increase
Solution Approach 1:
The system achieves sophisticated calibration through self-service mechanisms, automatically collecting and analyzing speech data during normal operation without requiring complex external training infrastructure. This self-calibrating approach maintains high recognition accuracy while avoiding the complexity and cost of manual training systems.
Solution Approach 2:
The patent replaces complex mechanical training systems with software-based automatic calibration. Instead of requiring physical training sessions with microphones and recording equipment, the system uses software to capture and analyze speech patterns from normal computer usage, reducing both device complexity and implementation cost while maintaining precision.
Data Source
AI summary
A system, method and program product for the shortcomings of the prior art are overcome and additional advantages are provided through a system, method and program product for initializing a speech recognition application for a computer. The method comprises recording a variety of sounds associated with a specific text; identifying location of different words as pronounced in different locations of this recorded specific text; and calibrating word location of an input stream based on results of the pre-recorded and identified word locations when attempting to parse words received from spoken sentences of the input stream.