Dynamic Wakeword Spotter for Speech Devices
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Solution Overview
Problem
Existing speech-enabled devices face challenges in customizing factory-installed wakewords, leading to collisions and poor user experiences due to the inability to quickly train new wakeword spotters without large datasets of audio samples.
Innovation Solution
A method is provided to modify wakewords by receiving a spoken request, parsing it into a natural language request and a speech audio segment, and building a new wakeword spotter to recognize the new wakeword as an activation trigger, using approaches such as wakeword segmentation, sampling, or continuous transcription.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If factory-installed wakewords are used, then devices can be deployed immediately, but multiple devices may collide and activate simultaneously leading to poor user experience
Solution Approach 1:
The system changes the wakeword parameter from a fixed factory-default value to a dynamically customizable value. Users can modify the wakeword to be unique across multiple devices, eliminating collisions while maintaining immediate device functionality. This parameter change allows the same device model to have different wakewords, resolving the contradiction between rapid deployment and reliable activation.
2Measurement precision
If traditional wakeword spotter training is used, then accurate wakeword recognition is achieved, but large datasets of audio samples are required which cannot be obtained for dynamic wakewords
Solution Approach 1:
The system performs preliminary action by pre-training wakeword spotters using factory-default wakewords and their associated audio datasets before device deployment. When users customize wakewords, the system leverages these pre-trained models as a foundation, requiring only minimal adaptation rather than complete retraining. This preliminary preparation enables accurate recognition of dynamic wakewords without needing large custom datasets for each user configuration.
Solution Approach 2:
The system creates a copy of the pre-trained wakeword spotter model and adapts it for the user's custom wakeword. Instead of training from scratch, the system copies the existing model's knowledge of wakeword patterns and acoustic features, then fine-tunes it for the specific custom wakeword. This copying approach maintains high recognition accuracy while minimizing the need for extensive new training data.
3Adaptability or versatility
If wakeword spotters are trained immediately without large datasets, then dynamic wakewords can be used, but traditional training approaches are not available
Solution Approach 1:
The system implements self-service by automatically generating training data and adapting models without requiring manual data collection or extensive user input. When a user customizes a wakeword, the system automatically uses the user's voice recording during the customization process as training data, and automatically adapts the spotter model. This self-service mechanism makes immediate spotter training feasible for dynamic wakewords by eliminating the need for separate data collection and manual training procedures.
Data Source
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AI summary
The present disclosure relates to dynamic wakewords for speech-enabled devices. A system and method are disclosed capable of parsing a spoken utterance into a natural language request and a speech audio segment, where the natural language request directs the system to use the speech audio segment as a new wakeword. In response to this wakeword assignment directive, the system and method are further capable of immediately building a new wakeword spotter to activate the device upon matching the new wakeword in the input audio. Different approaches to promptly building a new wakeword spotter are described. Variations of wakeword assignment directives can make the new wakeword public or private. They can also add the new wakeword to earlier wakewords, or replace earlier wakewords.