Minimal User-Specific Language Model for Speech Recognition
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Solution Overview
Problem
Conventional speech recognition systems rely on generic language models that are not user-specific, leading to errors and inefficiencies due to inclusion of n-grams that the actual user never uses or misses n-grams that the user does use, resulting in limited recognition accuracy for free form continuous speech.
Innovation Solution
A minimal user-specific language model is generated using a corpus of user-specific transcripts, which replaces the generic model when it has fewer missing n-grams, higher average n-gram probability, or lower perplexity, ensuring only n-grams actually produced by the user's speech patterns are used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic language model is used for speech recognition, then the system can accommodate a wide range of users and is deployable rapidly, but the recognition accuracy deteriorates because the model includes n-grams that users never use and misses user-specific n-grams
Solution Approach 1:
The language model is segmented into generic components (applicable to all users) and user-specific components (tailored to individual users). The system maintains a generic language model for broad compatibility while dynamically generating and integrating user-specific language models from collected transcripts, allowing the system to adapt to individual speech patterns without sacrificing general usability
Solution Approach 2:
The language model transitions from a static generic model to a dynamic system that evolves with user interaction. The system continuously collects user transcripts, generates updated user-specific language models, and integrates them with the generic model, allowing the recognition accuracy to improve over time as more user-specific data becomes available
2Measurement precision
If a user-specific language model is generated using user transcripts, then the speech recognition accuracy improves by including only relevant n-grams, but the system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential user-specific n-grams from collected transcripts that are not already present in the generic language model. By identifying and removing redundant n-grams and focusing only on user-specific patterns, the system reduces the complexity of user-specific model generation while maintaining improved recognition accuracy
Solution Approach 2:
The system changes parameters such as n-gram order, probability thresholds, and integration weights to optimize the balance between model specificity and computational complexity. By adjusting these parameters, the system can control the complexity of user-specific language model generation while preserving the accuracy benefits
3Reliability
If the language model is continuously updated with user transcripts, then the user-specific recognition performance improves, but the processing time and computational resources increase
Solution Approach 1:
Instead of continuously updating the language model with every user transcript, the system employs periodic updates at strategically determined intervals. The update frequency is adjusted based on factors such as the number of collected transcripts, performance thresholds, and user activity patterns, reducing unnecessary processing while maintaining recognition performance
Solution Approach 2:
The system uses feedback from recognition performance metrics to determine when and how to update the language model. By monitoring recognition accuracy and other performance indicators, the system can intelligently trigger updates only when necessary, avoiding wasteful processing while ensuring performance improvements are captured
Data Source
AI summary
The technology of the present application provides apparatuses and methods that may be used to generate the smallest language model for a continuous speech recognition engine that covers a given speaker's speech patterns. The apparatuses and methods start with a generic language model that is an approximation to the given speaker's speech patterns. The given speaker generates corrected transcripts that allows for the generation of a user specific language model. Once the user specific language model is sufficiently robust, the continuous speech recognition system may replace the generic language model with the user specific language model.