Speech Recognition Using Docking Context for Navigation
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately transcribing spoken queries, especially in specific contexts like vehicle navigation, due to the use of generalized language models that do not adapt to the user's likely speech patterns based on the docking context of the device.
Innovation Solution
The system accesses information about the docking context of a client device, selects a language model trained for a particular topical category based on this context, and assigns weighting values to improve the accuracy of speech recognition by biasing the process toward likely terms in that context, such as navigation-related words when connected to a vehicle docking station.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generalized language model is used for speech recognition, then the system can handle diverse speech inputs, but the transcription accuracy deteriorates in specific contexts like vehicle navigation
Solution Approach 1:
The system dynamically selects language models based on docking context. When the device is docked in a vehicle, a navigation-specific language model is automatically selected; otherwise, a generalized language model is used. This dynamic adaptation resolves the contradiction by switching between models rather than using a fixed generalized one.
Solution Approach 2:
The system applies different language model qualities to different operational contexts. A specialized navigation language model is applied locally when vehicle docking is detected, while a generalized model is used for other contexts. This local specialization improves transcription accuracy in specific contexts without sacrificing overall system versatility.
2Measurement precision
If multiple language models are maintained for different contexts, then transcription accuracy in specific contexts improves, but system complexity increases
Solution Approach 1:
The system automatically detects docking context and selects the appropriate language model without user intervention. The docking context detection mechanism self-services the language model selection process, reducing the perceived complexity for users while maintaining multiple specialized models in the background.
Solution Approach 2:
Language models are pre-configured and associated with specific docking contexts in advance. The system prepares multiple language models beforehand and links them to contextual scenarios (e.g., navigation model for vehicle docking), so that during operation, simple context matching determines model selection rather than complex real-time analysis.
Data Source
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AI summary
Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for performing speech recognition using dock context. In one aspect, a method includes accessing audio data that includes encoded speech. Information that indicates a docking context of a client device is accessed, the docking context being associated with the audio data. A plurality of language models is identified. At least one of the plurality of language models is selected based on the docking context. Speech recognition is performed on the audio data using the selected language model to identify a transcription for a portion of the audio data.