Domain-Specific Text Modification for Speech Recognition Accuracy
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately modifying speech recognition results across different domains, leading to inconsistencies and inaccuracies in text output, particularly when dealing with diverse voice inputs and contexts.
Innovation Solution
A system and method where a device and server interoperate to utilize an automatic speech recognition (ASR) model and an artificial intelligence (AI) model for text modification, identifying the relevant domain and selecting appropriate text modification models to refine the output text, ensuring accuracy and context relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single speech recognition model is used across different domains, then the system complexity is reduced, but the recognition accuracy and context relevance deteriorate in domain-specific scenarios
Solution Approach 1:
The patent divides the speech recognition system into multiple domain-specific models (e.g., general domain model, news domain model, sports domain model) instead of using a single unified model. Each model is trained on domain-specific data to improve recognition accuracy for that particular domain while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system dynamically selects which domain-specific model to use based on the input context and domain identification. The model selection is not static but adapts in real-time according to the speech content, allowing the system to optimize between using a simple general model or a specialized domain model as needed.
2Measurement precision
If domain-specific text modification models are selected and applied, then the text output accuracy and context relevance are improved, but the device complexity and model selection overhead increase
Solution Approach 1:
The system performs domain identification and model selection in advance before the main speech recognition processing. By pre-classifying the input speech into domains and pre-selecting the appropriate model, the system avoids complex real-time adjustments during recognition, thereby improving text output accuracy while managing complexity through upfront preparation.
Solution Approach 2:
The patent introduces a domain identification module as an intermediary between the speech input and the recognition models. This mediator analyzes the input speech to determine the appropriate domain, then routes the input to the corresponding domain-specific model, simplifying the overall system architecture while maintaining high accuracy through specialized processing.
3Adaptability or versatility
If multiple domain-specific models are maintained and switched between, then the adaptability to different speech contexts is improved, but the loss of time for model switching and selection occurs
Solution Approach 1:
The system performs domain identification and model selection in advance before the main speech recognition processing. By pre-classifying the input speech into domains and pre-selecting the appropriate model, the system avoids complex real-time adjustments during recognition, thereby improving text output accuracy while managing complexity through upfront preparation.
Data Source
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AI summary
Provided are a system and method for modifying a speech recognition result. The method includes: receiving, from a device, text output from an automatic speech recognition (ASR) model of the device; identifying at least one domain related to the received text; selecting, from among a plurality of text modification models included in the server, at least one text modification model corresponding to the identified at least one domain; and modifying the received text by using the selected at least one text modification model.