Language Model Adaptation via Corpus Indexing
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Solution Overview
Problem
Existing language models for speech recognition lack the ability to effectively adapt to specific contexts, leading to reduced recognition fidelity when dealing with target texts, as they are typically trained on general texts unrelated to the subject matter.
Innovation Solution
A method is introduced to enhance a baseline language model by incorporating relevant textual expressions from a textual corpus, which are identified based on relevancy determinations such as perplexities, stem matches, and semantic similarities, and then indexed for efficient retrieval and adaptation, thereby improving the model's context recognition capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a baseline language model is trained on general texts unrelated to the subject matter, then the model can be quickly deployed with simple structure, but the recognition fidelity deteriorates when dealing with target texts in specific contexts
Solution Approach 1:
The patent applies preliminary action by pre-processing the textual corpus before adaptation. The system performs normalization, tokenization, and indexing of the corpus in advance, creating a structured format that facilitates efficient retrieval during the adaptation process. This preliminary preparation reduces the complexity of the actual adaptation operation while improving recognition fidelity.
Solution Approach 2:
The patent introduces an intermediary indexing structure that mediates between the baseline language model and the textual corpus. The index serves as a bridge, allowing the model to efficiently access relevant textual expressions without directly processing the entire corpus. This intermediary structure simplifies the adaptation process while enhancing recognition accuracy for context-specific terms.
2Reliability
If relevant textual expressions are incorporated from a textual corpus into the baseline language model, then the context recognition capability is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary indexing of the textual corpus, organizing textual expressions in advance for efficient retrieval. This pre-processing step creates a structured index that enables rapid access during adaptation, significantly reducing the processing time required to incorporate relevant expressions while maintaining improved context recognition capability.
Solution Approach 2:
The patent extracts only the relevant textual expressions from the corpus based on relevancy determinations, rather than incorporating the entire corpus. This selective extraction process identifies and retrieves only those expressions that are pertinent to the target text context, reducing processing time and computational resources while still improving context recognition capability.
3Measurement precision
If multiple relevancy determinations are performed between documents and target text, then the precision of text selection is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the relevancy determination process into multiple independent criteria: perplexity-based determination, stem matching determination, and semantic similarity determination. Each criterion operates independently and can be computed separately, allowing the system to achieve high text selection precision through multiple determinations while managing computational complexity through modular processing.
Solution Approach 2:
The system performs multiple relevancy determinations but applies them in a staged manner, where not all determinations are required for every document. The patent allows for partial application of relevancy criteria, performing only the necessary determinations based on initial filtering, thus achieving high precision in text selection while reducing overall computational complexity.
Data Source
AI summary
A computerized method for adapting a baseline language model, comprising obtaining a textual corpus of documents that comprise textual expressions, incorporating in the baseline language model textual expressions from documents which are determined as relevant to a provided target text based on a plurality of different relevancy determinations between the documents and the provided target text, thereby adapting the baseline language model to form an adapted language model for recognizing terms of a context of the provided target text, wherein the method is automatically performed on an at least one computerized apparatus configured to perform the method.