Context-Aware Language Recognition for Speech Accuracy
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Solution Overview
Problem
Natural language processing systems face challenges in tailoring their operation to specific users, particularly when multiple users share a device or account, leading to errors in speech recognition and understanding due to the use of communal language processing data.
Innovation Solution
The system introduces a language recognition context (LRC) architecture that groups language processing data (LPD) based on similar situations or contexts, allowing for personalized LPD to be used in specific contexts, such as when a user is driving or at home.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If communal language processing data is used for multiple users, then device complexity is reduced and ease of operation is improved, but speech recognition accuracy deteriorates due to inability to adapt to user-specific language habits
Solution Approach 1:
The patent segments language processing data into multiple context-specific datasets (e.g., driving context, home context, work context) rather than using a single communal dataset. Each context has its own language processing model trained on user-specific data from that context, allowing the system to select the appropriate model based on the current situation, thereby improving accuracy without requiring all data to be processed simultaneously
Solution Approach 2:
The system dynamically selects and switches between different language processing contexts based on detected situational parameters (location, time, activity). The context selection is not static but adapts in real-time based on sensor data and user behavior patterns, allowing the system to optimize for the current situation while maintaining manageable complexity through selective processing
2Reliability
If personalized language processing data is used for each user context, then speech recognition accuracy is improved, but device complexity increases due to need to manage multiple language processing datasets
Solution Approach 1:
The patent creates a universal context detection framework that identifies situational parameters (location, time, activity) applicable across all user contexts. This single detection mechanism serves multiple functions: determining which context is active, selecting the appropriate language processing model, and triggering context-specific processing, thereby reducing the complexity overhead of managing multiple personalized datasets
Solution Approach 2:
The system performs preliminary context detection and classification before initiating language processing. By pre-identifying the active context (e.g., detecting that the user is driving based on location and motion sensors), the system can pre-select the appropriate language processing model and prepare context-specific parameters, avoiding the need to manage all possible contexts simultaneously during actual speech processing
Data Source
AI summary
This disclosure proposes systems and methods for processing natural language inputs using data associated with multiple language recognition contexts (LRC). A system using multiple LRCs can receive input data from a device, identify a first identifier associated with the device, and further identify second identifiers associated with the first identifier and representing candidate users of the device. The system can access language processing data used for natural language processing for the LRCs corresponding to each of the first and second identifiers, and process the input data using the language processing data at one or more stages of automatic speech recognition, natural language understanding, entity resolution, and/or command execution. User recognition can reduce the number of candidate users, and thus the amount of data used to process the input data. Dynamic arbitration can select from between competing hypotheses representing the first identifier and a second identifier, respectively.