ConvMixer: Feature Interactive Convolution with Curriculum Learning for Small Footprint and Noisy Far-field Keyword Spotting

Understand

Building efficient architecture in neural speech processing is paramount to success in keyword spotting deployment.

• However, it is very challenging for lightweight models to achieve noise robustness with concise neural operations.
• In a real-world application, the user environment is typically noisy and may also contain reverberations.
• We proposed a novel feature interactive convolutional model with merely 100K parameters to tackle this under the noisy far-field condition.