Context-Aware Speech Recognition Engine Adjustment
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Solution Overview
Problem
Automated speech recognition engines face challenges in noisy environments and often misinterpret user commands due to lack of contextual understanding, leading to incorrect word or command recognition.
Innovation Solution
The system obtains contextual information about the user's activity, environment, and device state to adjust the automated speech recognition engine, biasing it towards more likely commands and words based on the context, thereby improving recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated speech recognition engine processes user speech input without contextual information, then device complexity is reduced, but speech recognition accuracy deteriorates in noisy environments
Solution Approach 1:
The system performs preliminary actions by obtaining contextual information about the user's activity, environment, and device state before processing speech input. This contextual information is used to adjust the ASR engine's parameters and bias towards more likely commands, improving recognition accuracy before the speech input is even received.
Solution Approach 2:
The system changes parameters of the ASR engine based on contextual information. Specifically, it adjusts the language model parameters, vocabulary weighting, and command probability distributions according to the detected context, allowing the engine to adapt its recognition behavior to different situations without increasing fundamental system complexity.
2Measurement precision
If automated speech recognition engine uses contextual information to adjust recognition, then speech recognition accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial action by selectively adjusting only the most relevant ASR parameters based on the detected context, rather than performing exhaustive analysis of all possible contextual factors. This allows the system to gain significant accuracy improvements while minimizing the time overhead of contextual processing.
3Reliability
If automated speech recognition engine is adjusted based on contextual information, then command recognition reliability is improved, but adaptability to different contexts is reduced
Solution Approach 1:
The system implements dynamic adaptation by continuously monitoring contextual information and adjusting ASR engine parameters in real-time. The contextual analyzer dynamically updates the language model and vocabulary weighting based on current user activity, environment, and device state, allowing the system to adapt to different contexts while maintaining high reliability within each context.
Data Source
AI summary
An embodiment provides a method, including: obtaining, using a processor, contextual information relating to an information handling device; adjusting, using a processor, an automated speech recognition engine using the contextual information; receiving, at an audio receiver of the information handling device, user speech input; and providing, using a processor, recognized speech based on the user speech input received and the contextual information adjustment to the automated speech recognition engine. Other aspects are described and claimed.