Wake-up Model Training Using False Activation Counterexamples
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Solution Overview
Problem
Existing device wake-up methods frequently result in false awakenings, which are costly and affect travel safety, as they require additional corpus recording and are not effectively suppressed.
Innovation Solution
A method where voice information during false awakenings is used as counterexample samples to train a wake-up model, eliminating the need for additional corpus recording and automatically suppressing false awakenings, thereby improving wake-up accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional wake-up models are used, then device can be woken up by voice commands, but false wake-up events occur frequently
Solution Approach 1:
The patent converts false wake-up events from harmful errors into beneficial training data. By collecting voice recordings during false wake-up events and using them as negative samples in model training, the system transforms mistakes into learning opportunities, continuously improving wake-up accuracy while reducing future false activations.
Solution Approach 2:
The patent implements a feedback loop where wake-up events (including false wake-ups) are collected, analyzed, and used to retrain the wake-up model. This closed-loop system continuously improves model performance by feeding back real-world performance data, particularly using false wake-up cases to adjust decision boundaries and reduce future errors.
2Reliability
If additional corpus recording is performed to improve wake-up accuracy, then model performance improves, but development cost and time increase
Solution Approach 1:
The patent enables the system to self-improve without external intervention. The wake-up model automatically collects its own performance data during operation, including false wake-up events, and uses this data for self-training and retraining. This eliminates the need for manual corpus recording and external data collection efforts.
Solution Approach 2:
The patent changes the training data parameters by incorporating false wake-up voice recordings as negative samples. Instead of requiring additional carefully recorded corpus data, the system utilizes previously wasted or discarded false wake-up events, transforming them into valuable training material that adjusts model parameters and improves accuracy.
Data Source
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AI summary
Embodiments of the present disclosure disclose a method and apparatus for outputting information, which can be used in an intelligent traffic scene. An embodiment of the method includes acquiring voice information received within a preset time period before a device is awakened, where the device is provided with a wake-up model for outputting preset response information when a preset wake-up word is received; performing speech recognition on the voice information to obtain a recognition result; extracting feature information of the voice information in response to determining that the recognition result does not include a preset wake-up word; generating a counterexample training sample according to the feature information; and training the wake-up model using a counter-example training sample, and outputting the trained wake-up model. In this embodiment, the voice information during false wakeup can be used as an example to train a wake-up model, no additional corpus recording is required, cost is saved, false wake-up can be automatically suppressed, and accuracy of wakeup is improved.