Voice Recognition Wake-Up Word Segmentation and Silence Labeling
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Solution Overview
Problem
Current voice recognition systems face inefficiencies due to the need to process entire user voices, including irrelevant information, which reduces recognition speed and accuracy.
Innovation Solution
A method where the wake-up voice is identified and labeled with a silence identifier, allowing it to be ignored during recognition, thereby reducing data processing and improving efficiency using a decoding algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the server recognizes the entire user voice to ensure complete information processing, then the recognition accuracy is improved, but the data processing volume increases and recognition efficiency decreases
Solution Approach 1:
The patent segments the user voice into two distinct parts: the wake-up voice and the actual instruction voice. By identifying and separating the wake-up voice (which contains irrelevant information for the recognition task), the system can process only the relevant instruction portion, thereby reducing data processing volume while maintaining recognition accuracy for the actual command.
Solution Approach 2:
The patent extracts and removes the wake-up voice from the overall user voice input. By taking out the irrelevant wake-up voice segment and labeling it with a silence identifier, the system extracts only the necessary instruction information for processing, reducing the data amount that needs to be analyzed by the decoding algorithm while preserving the accuracy of recognizing the actual user instruction.
2Loss of information
If the server processes the entire user voice including wake-up words, then no information is lost, but the data amount to be processed increases reducing efficiency
Solution Approach 1:
The patent performs preliminary action by identifying and labeling the wake-up voice with a silence identifier before the main recognition process. This preliminary segmentation and labeling step allows the system to pre-determine which portions of the audio should be ignored during decoding, ensuring that no relevant information is lost while significantly reducing the data volume that requires full processing.
Solution Approach 2:
The patent applies local quality by treating different parts of the user voice differently. The wake-up voice portion is marked with a silence identifier indicating it should be ignored, while the instruction voice portion maintains its full processing quality. This localized differentiation allows the system to maintain information completeness for relevant segments while improving processing efficiency by excluding irrelevant segments.
Data Source
AI summary
A voice recognition method, device, and a server are provided. The method includes: receiving a user voice; determining a wake-up voice of a wake-up word in the user voice, according to an acoustic feature of the user voice; and labeling the wake-up voice with a silence identifier; and ignoring the wake-up voice based on the silence identifier during voice recognition. As such, when a complex decoding algorithm is used to recognize the user voice, recognition of the wake-up word that is irrelevant to an instruction of the user is omitted, thus reducing the data amount to be processed by the decoding algorithm and improving the efficiency of voice recognition.