Speech Recognition Using Dynamic Language Model Weighting
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Solution Overview
Problem
Current speech recognition technologies using neural network models face challenges in accurately interpreting input speech due to the specialized and automated nature of computational architectures, which can lead to inefficiencies in handling varying contexts and domains.
Innovation Solution
A speech recognition method that dynamically determines weights for language models based on input speech and context information, including user input, location, time, and speech recognition history, to select the most appropriate candidate texts, thereby improving recognition accuracy across different domains and situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If specialized computational architectures are used for automated speech recognition, then processing efficiency is improved, but adaptability to varying contexts and domains deteriorates
Solution Approach 1:
The patent applies dynamics by making the language model selection and weighting adaptive rather than fixed. The system dynamically selects from multiple language models and adjusts their weights based on context analysis, allowing the speech recognition system to adapt to varying domains and situations while maintaining automated processing efficiency.
Solution Approach 2:
The system changes parameters by adjusting the weights of different language models based on contextual analysis. Instead of using a single fixed language model, the system varies the contribution of each language model according to the specific context, domain, and situation detected in the input speech, thereby improving adaptability while maintaining automation.
2Device complexity
If a single language model is used for speech recognition, then device complexity is reduced, but recognition accuracy in diverse domains deteriorates
Solution Approach 1:
The patent implements universality by creating a speech recognition system that can handle multiple domains and contexts through a single unified framework. Instead of requiring separate specialized systems for each domain, the system uses multiple language models within one architecture, selecting and weighting them based on the input context to achieve accurate recognition across diverse applications.
Solution Approach 2:
The system uses composite materials analogy by combining multiple language models into a unified speech recognition system. Each language model contributes its strengths to specific domains, and the system composes their outputs with context-dependent weights to achieve accurate recognition across diverse situations without requiring a completely separate system for each domain.
3Measurement precision
If context information is extensively analyzed to improve accuracy, then processing time increases, but recognition precision improves
Solution Approach 1:
The system applies preliminary action by pre-analyzing context information and pre-selecting appropriate language models before the actual speech recognition task. The context analysis module prepares the optimal language model configuration in advance, so when speech input is received, the system can quickly proceed with recognition using the pre-selected models, reducing real-time processing time while maintaining high precision.
Data Source
AI summary
A speech recognition method and apparatus are disclosed. The speech recognition method includes determining a first score of candidate texts based on an input speech, determining a weight for an output of a language model based on the input speech, applying the weight to a second score of the candidate texts output from the language model to obtain a weighted second score, selecting a target candidate text from among the candidate texts based on the first score and the weighted second score corresponding to the target candidate text, and determining the target candidate text to correspond to a portion of the input speech.