Discriminative Adaptation of Language Model Weights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional statistical language models in speech recognition systems are limited by their inability to fully utilize user feedback and adapt to new contexts, leading to suboptimal recognition accuracy and empirical errors, especially when lacking sufficient data or appropriate weighting of context-free grammar terms.
Innovation Solution
A discriminative adaptation algorithm is employed to optimize the weighting of terms and phrases in statistical language models and context-free grammar, allowing for self-adjustment and minimization of empirical errors without requiring acoustic data, by using an objective function that compares true transcriptions with competitive strings based on acoustical and language model scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a statistical language model is trained separately as a single component, then the model structure is simple, but the recognition accuracy cannot be fully optimized because it does not use information from acoustical models, lexicon, and recognizer
Solution Approach 1:
The patent combines the language model training process with acoustical models, lexicon, and recognizer to create an integrated system. The discriminative training algorithm uses information from all these components simultaneously, allowing the language model to learn from the interaction between different system elements rather than being trained in isolation.
Solution Approach 2:
The patent implements a feedback mechanism where the recognizer and acoustical models provide information back to the language model training process. The discriminative training algorithm uses the recognition results and acoustic information to adjust language model parameters, creating a closed-loop system that continuously improves accuracy.
2Adaptability or versatility
If a statistical language model uses maximum likelihood estimation, then the model is easy to train with sufficient data, but the model cannot adapt to new contexts without retraining because it needs enough data to support parameter estimations
Solution Approach 1:
The patent changes the training objective from maximum likelihood estimation to discriminative training with a different objective function. This parameter change in the training approach allows the model to adapt to new contexts by learning from recognition errors and feedback without requiring large amounts of new training data, as the discriminative approach is more data-efficient for adaptation.
Solution Approach 2:
The patent performs preliminary adaptation by incorporating feedback from the recognizer and acoustical models during the training process. This preliminary action allows the language model to pre-adapt to specific contexts and applications before actual use, reducing the need for extensive retraining when encountering new contexts.
3Reliability
If context-free grammar weights are assigned equally or arbitrarily, then the CFG implementation is simple, but the speech recognizer cannot provide reasonable results when weights do not reflect term similarity or importance
Solution Approach 1:
The patent applies local quality by assigning different weights to different terms and phrases in the context-free grammar based on their specific characteristics, such as similarity to other terms, importance in the context, and frequency of occurrence. This localized weight assignment allows each grammar element to have the appropriate weight for its specific role rather than using uniform weights.
Solution Approach 2:
The patent uses feedback from the recognition process to adjust CFG weights. The discriminative training algorithm analyzes recognition results and uses this feedback to iteratively optimize the weights assigned to different grammar terms, improving recognition quality over time based on actual system performance.
4Measurement precision
If conventional language models are used without discriminative adaptation, then the system is easier to operate, but empirical errors increase and recognition accuracy decreases
Solution Approach 1:
The patent implements self-service by enabling the language model to automatically adapt and optimize itself through discriminative training. The system uses its own recognition results and feedback to improve its performance without requiring manual intervention or complex external optimization processes, making the improved accuracy achievable through automated self-improvement.
Data Source
AI summary
Architecture is disclosed herewith for minimizing an empirical error rate by discriminative adaptation of a statistical language model in a dictation and/or dialog application. The architecture allows assignment of an improved weighting value to each term or phrase to reduce empirical error. Empirical errors are minimized whether a user provides correction results or not based on criteria for discriminatively adapting the user language model (LM)/context-free grammar (CFG) to the target. Moreover, algorithms are provided for the training and adaptation processes of LM/CFG parameters for criteria optimization.