Speech Instruction Correction via Semantic Analysis and Neural Networks
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Solution Overview
Problem
Intelligent speech interactive systems often fail to execute intended operations due to incorrect speech recognition, leading to wasted resources and a poor user experience, as users must reinput instructions to correct errors.
Innovation Solution
A method and apparatus that recognize speech instructions, perform semantic analysis, determine if the operation is executable, and if not, identify if the text information belongs to instruction data, correct it, and then re-analyze and execute the operation based on the corrected text, utilizing a pre-trained convolutional neural network or correction rules/models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is performed without correction mechanism, then processing speed is fast, but recognition accuracy deteriorates leading to wrong operations
Solution Approach 1:
The system performs preliminary correction of the speech recognition result before executing the operation. A correction module pre-processes the recognized text to fix potential errors, and only then does the execution module carry out the operation. This preliminary action ensures accuracy without requiring user intervention, maintaining both high accuracy and processing speed.
Solution Approach 2:
The system implements a feedback mechanism where the execution module monitors the speech recognition result and provides feedback to the correction module. When an error is detected in the recognized text, the feedback loop triggers automatic correction before operation execution, ensuring accurate results while maintaining efficient processing through automated rather than manual correction.
2Ease of operation
If automatic correction is implemented, then user operations are reduced, but system complexity increases
Solution Approach 1:
The system implements self-service correction where the correction module automatically processes and corrects speech recognition results without user involvement. The system serves itself by detecting and fixing its own recognition errors, eliminating the need for user intervention while keeping the interface simple and the overall system structure manageable.
Solution Approach 2:
The correction module acts as an intermediary between the speech recognition module and the execution module. It receives recognized text, performs automatic correction, and passes the corrected text to the execution module. This intermediary layer handles the complexity of correction algorithms internally while presenting a simple interface to users, reducing user operations without exposing system complexity.
Data Source
AI summary
A method and a device for processing a speech instruction are provided. With the method, a speech instruction is recognized so as to generate text information corresponding to the speech instruction. Semantic analysis is performed on the text information so as to obtain a semantic analysis result of the text information. Whether a corresponding operation is executable based on the semantic analysis result of the text information is determined. If it is determined that the corresponding operation is not executable based on the semantic analysis result of the text information, whether the text information belongs to instruction text data is determined. If yes, the text information is corrected so as to obtain corrected text information. Semantic analysis is performed on the corrected text information, and the corresponding operation is executed according to a semantic analysis result of the corrected text information.