Integrated Language Model Combining FSG and SLM
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Solution Overview
Problem
Current speech recognition systems face challenges with the finite state grammar (FSG) and statistical language model (SLM) formats, where FSG offers high accuracy but is labor-intensive and memory-demanding, while SLM provides broader recognition but with lower accuracy, and there is a need for a combination that leverages both formats effectively.
Innovation Solution
The development of integrated language models that combine FSG and SLM formats by using a processor and machine-readable memory to generate upper-level and lower-level language model components, with non-terminals allowing for automatic text element replacement and application rules to enhance recognition accuracy and reduce grammar size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FSG format is used for language model, then recognition accuracy is improved, but memory demand and processing complexity increase
Solution Approach 1:
The language model is divided into multiple grammar files, each handling specific portions of the vocabulary and rules. This segmentation allows the system to load only necessary grammar portions into memory, reducing overall memory demand while maintaining high recognition accuracy for domain-specific speech recognition.
Solution Approach 2:
The system introduces a hierarchical dimension to the grammar structure by organizing grammar rules across multiple levels and files. This dimensional organization enables efficient memory management by allowing selective loading of grammar components based on the specific recognition task, thereby reducing memory requirements while preserving accuracy.
2Measurement precision
If FSG format is used for language model, then recognition accuracy is improved, but compilation time and labor intensity increase
Solution Approach 1:
Grammar files are pre-compiled and organized into modular units before runtime. This preliminary organization allows the system to quickly assemble the necessary grammar components during operation without requiring time-consuming compilation processes, thus reducing compilation time while maintaining the high accuracy benefits of FSG format.
3Adaptability or versatility
If SLM format is used for language model, then recognition range is improved, but recognition accuracy decreases
Solution Approach 1:
The system merges the advantages of both FSG and SLM formats by integrating structured grammar files with statistical language modeling capabilities. This combination allows the system to maintain high recognition accuracy through structured grammar rules while simultaneously achieving broader recognition range through statistical language understanding, effectively resolving the trade-off between accuracy and versatility.
Data Source
AI summary
An integrated language model includes an upper-level language model component and a lower-level language model component, with the upper-level language model component including a non-terminal and the lower-level language model component being applied to the non-terminal. The upper-level and lower-level language model components can be of the same or different language model formats, including finite state grammar (FSG) and statistical language model (SLM) formats. Systems and methods for making integrated language models allow designation of language model formats for the upper-level and lower-level components and identification of non-terminals. Automatic non-terminal replacement and retention criteria can be used to facilitate the generation of one or both language model components, which can include the modification of existing language models.