Speech Wakeup Model Using DNN and CTC for Low-Power Devices
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Solution Overview
Problem
Existing speech wakeup technologies rely heavily on keyword-specific speech data, limiting their accessibility and flexibility, and are not well-suited for use in low-power devices.
Innovation Solution
A speech wakeup model trained with both general speech data and keyword-specific speech data using a Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC), allowing for user-defined keywords and efficient operation on low-power devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-specific speech data is used for training, then speech wakeup accuracy is improved, but data accessibility and system flexibility deteriorate
Solution Approach 1:
The patent trains the speech wakeup model using general speech data that can serve multiple purposes - both as general speech recognition data and as keyword-specific data through data augmentation techniques. This universal approach allows the same training corpus to improve speech wakeup accuracy while maintaining system flexibility and adaptability across different keywords and scenarios.
2Measurement precision
If complex speech wakeup models are used, then speech wakeup accuracy is improved, but computational burden and power consumption increase
Solution Approach 1:
The patent performs extensive model training and optimization in advance using general speech data, creating a pre-trained speech wakeup model that can be deployed on low-power devices. By doing the computationally intensive work beforehand, the model achieves high accuracy while requiring minimal computational resources during actual speech wakeup operations on power-constrained devices.
3Measurement precision
If keyword-specific speech data is used for training, then speech wakeup accuracy is improved, but data accessibility deteriorates
Solution Approach 1:
The patent uses general speech data that is widely available from public corpora and diverse sources, making data acquisition easy and accessible. Through data augmentation and universal training approaches, this easily accessible general data is transformed into effective training material that achieves speech wakeup accuracy previously requiring scarce keyword-specific data.
Data Source
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AI summary
A speech wakeup method, apparatus, and electronic device are disclosed in embodiments of this specification. The method includes: implementing speech wakeup by using a speech wakeup model that includes a Deep Neural Network (DNN) and a Connectionist Temporal Classifier (CTC). The speech wakeup model can be obtained by training with general speech data.