AI Speech Technology Teaching Implementation Pathway
The modular design of wukong-robot is ideal for use as a teaching tool, and the following practical solutions are recommended:
- Tiered Teaching Objectives::
- Base layer: speech signal processing (analysis of .wav file characteristics)
- Middle Layer: NLU Intent Recognition (Modified)DialogueManager)
- Advanced layer: development of complete plug-ins such as weather search - Typical experimental design::
1. Voice cloning experiment: using the VITS module to generate personalized speech
2. Wake-up call training: production of class-specific wake-up calls based on Porcupine
3. Dialogue logic design: access to handwriting calculator as a skill plug-in - Hardware Expansion Program::
Combined with the traveling empty board to realize physical interaction (shake to wake up), or through the GPIO to control the LED to respond to voice commands. The brain-computer interaction module can carry out EEG signal analysis experiments.
Teaching recommendations: 1) Use Docker to unify the development environment; 2) Establish a plug-in development scaffold; 3) Refer to the project wiki for theEducation.mdGet course examples. Works with Jupyter Notebook for teaching algorithm visualization.
This answer comes from the articlewukong-robot: a smart speaker project to create personalized Chinese voice conversationsThe
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