/Deep-Keyword-Spotting

A Small Footprint implementation of Keyword Spotting with different architectures.

Primary LanguageJupyter Notebook

Small Footprint Keyword Spotting with different ML architectures

This repository contains the implementation in Tensorflow 2.11.0 of different models for the KWS task.

CNN with dropout(0.2) - 4 classes (3 keywords + 1) - test acc: 97.72%

  • Total params: 1,394,592
  • Trainable params: 1,394,216
  • Non-trainable params: 376

CRNN - 4 classes (3 keywords + 1) - test acc: 96.23%

  • Total params: 608,484
  • Trainable params: 608,484
  • Non-trainable params: 0

Autoencoder (+ SVM) - 4 classes (3 keywords + 1) - test acc: 92.36% - code dim: 12

  • Total params: 1,531,381
  • Trainable params: 1,531,381
  • Non-trainable params: 0

PCA + SVM - 257 dim - test acc: 88.89%

  • Trainable params: 66,049

PCA + SVM - 25 dim - test acc: 86.35%

  • Trainable params: 625