Log-Linear Model Initialization via N-Gram Backoff
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Solution Overview
Problem
Training log-linear language models on large datasets using convex optimization methods is computationally expensive, and existing methods for initializing these models from backoff n-gram models do not fully leverage the potential of additional linguistic and non-linguistic features to improve performance.
Innovation Solution
The method involves obtaining an n-gram language model, determining features and weights for a log-linear model based on the n-gram parameters, and using these to generate a log-linear model that can be trained to improve transcription accuracy for utterances, incorporating backoff features and non-linguistic context to enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If log-linear language models are trained on large datasets using convex optimization methods, then model accuracy and performance are improved, but computational cost and training time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training an n-gram language model on large datasets before initializing the log-linear model. This pre-computed n-gram model serves as a foundation, allowing the log-linear model to start with informed parameter estimates rather than random initialization, thereby reducing the computational burden of subsequent training while maintaining or improving transcription accuracy.
Solution Approach 2:
The patent uses the n-gram language model as an intermediary between raw training data and the final log-linear model. The n-gram model acts as a mediator that processes large datasets and extracts useful patterns, which are then transferred to initialize the log-linear model parameters, reducing the direct computational burden on the log-linear training process.
2Productivity
If traditional n-gram models are used for speech recognition, then computational efficiency is maintained, but the ability to incorporate additional linguistic and non-linguistic features is limited
Solution Approach 1:
The patent applies parameter changes by transforming the model formulation from traditional n-gram to log-linear parameters. This parameter transformation enables the model to incorporate additional linguistic and non-linguistic features while maintaining computational efficiency. The log-linear parameterization allows flexible feature integration without the exponential complexity growth associated with traditional approaches.
3Adaptability or versatility
If log-linear models are initialized from scratch without using pre-trained n-gram models, then model flexibility is maintained, but training time and computational resources are significantly increased
Solution Approach 1:
The patent applies preliminary action by pre-training an n-gram language model on large datasets before initializing the log-linear model. This pre-computed n-gram model serves as a foundation, allowing the log-linear model to start with informed parameter estimates rather than random initialization, thereby reducing the computational burden of subsequent training while maintaining or improving transcription accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to generating log-linear models. In some implementations, n-gram parameter values derived from an n-gram language model are obtained. N-gram features for a log-linear language model are determined based on the n-grams corresponding to the obtained n-gram parameter values. A weight for each of the determined n-gram features is determined, where the weight is determined based on (i) an n-gram parameter value that is derived from the n-gram language model and that corresponds to a particular n-gram, and (ii) an n-gram parameter value that is derived from the n-gram language model and that corresponds to an n-gram that is a sub-sequence within the particular n-gram. A log-linear language model having the determined n-gram features is generated, where the determined n-gram features in the log-linear language model have weights that are initialized based on the determined weights.