Voice Recognition Neural Network for Foreign Language Segments
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Solution Overview
Problem
Existing voice recognition models face difficulties in accurately recognizing and converting foreign language segments within audio files, especially when multiple languages are present, as they lack the capability to differentiate and handle different language types effectively.
Innovation Solution
A voice recognition method utilizing a neural network that generates initial text data based on voice information, detects problem text by deriving reliability, and generates corrected text data for detected foreign languages, allowing for accurate extraction and conversion of foreign language voices into text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single language model is used for voice recognition, then the system is simple and fast, but it cannot properly recognize foreign language segments
Solution Approach 1:
The patent divides the voice recognition task into multiple segments based on language types. It segments the audio input into different language regions and applies specialized language models to each segment. This allows the system to maintain high recognition accuracy for each language while keeping individual model segments relatively simple.
Solution Approach 2:
The patent creates a universal voice recognition system that can handle multiple languages through a language identification and routing mechanism. The system identifies the language type first, then routes to the appropriate language model, making the overall system multi-functional without requiring each component to be overly complex.
2Reliability
If multiple language models are implemented, then foreign language recognition improves, but the system becomes more complex and slower
Solution Approach 1:
The patent performs language identification as a preliminary action before applying the full voice recognition process. By identifying the language type first, the system can select and prepare the appropriate language model in advance, avoiding the need to process through multiple models sequentially and thus maintaining fast recognition speed.
Solution Approach 2:
The patent implements a dynamic language model selection mechanism that adapts to the input audio in real-time. Based on language identification results, the system dynamically selects and switches between different language models during the recognition process, optimizing both accuracy and speed for each specific input.
3Measurement precision
If language-specific processing is added, then foreign language accuracy improves, but the processing time increases
Solution Approach 1:
The patent segments the processing pipeline into language identification, language-specific parameter adjustment, and recognition execution. This segmentation allows the system to apply language-specific processing only to the necessary components rather than the entire pipeline, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent changes recognition parameters based on detected language types. Instead of using fixed parameters, the system adjusts language-specific parameters such as phoneme sets, pronunciation models, and language rules dynamically, enabling accurate foreign language recognition without adding excessive processing overhead.
Data Source
AI summary
Disclosed is a voice recognition method based on a neural network, performed by one or more processors of a computing device. The voice recognition method includes generating first text data based on a first language based on voice information. The method includes detecting problem text among the generated first text data. The method includes detecting a second language corresponding to the detected problem text. The method includes generating second text data based on the detected second language, in which the second language is different from the first language.