Dynamic Language Model Merging for ASR Accuracy
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Solution Overview
Problem
Automatic Speech Recognition (ASR) systems face challenges in accurately transcribing speech when the language is unknown or contains words not included in existing models, leading to reduced performance and accuracy.
Innovation Solution
The method involves using information about the speaker or recipient, such as metadata and external data sources, to create a tailored language model by combining general and specific language models, and applying this model to improve ASR performance, particularly by incorporating known words and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general language model is used for speech recognition, then the system can handle a wide range of speech inputs, but accuracy decreases when encountering application-specific terms or unknown words
Solution Approach 1:
The patent combines a general language model with application-specific language models to create a hybrid model that maintains broad coverage while improving accuracy for domain-specific terms. The system merges multiple language models dynamically based on the speech input context.
Solution Approach 2:
The system dynamically adapts the language model by selecting or weighting different models based on the speech input characteristics. This allows the system to switch between general and specific models, optimizing both coverage and accuracy for different speech scenarios.
2Measurement precision
If speech recognition models include all possible words, then accuracy improves, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent segments the language model into multiple specialized models (general model, application-specific models, domain-specific models) rather than using one comprehensive model. This segmentation reduces the complexity of each individual model while maintaining overall accuracy through selective model usage.
Solution Approach 2:
Different parts of the speech recognition system use different language models optimized for their specific purposes. The system applies local quality by matching the appropriate model complexity to the specific speech context, using simpler models for common speech and more specialized models for domain-specific content.
3Measurement precision
If the language model is adapted to specific applications, then transcription accuracy improves for that application, but the system loses ability to handle other languages or domains
Solution Approach 1:
The system creates a universal framework that can load and switch between multiple language models serving different applications, languages, and domains. This multi-functional approach allows a single speech recognition system to handle diverse speech inputs by selecting the appropriate specialized model for each context.
Data Source
AI summary
A method for producing speech recognition results on a device includes receiving first speech recognition results, obtaining a language model, wherein the language model represents information stored on the device, and using the first speech recognition results and the language model to generate second speech recognition results.