Speech commands classification with recurrent neural networks

The file Speech_commands_classification_with_recurrent_neural_networks.pdf is the report explaining used architectures, showing obtained results and comparisons.

Dataset: Speech Commands Dataset

https://www.kaggle.com/c/tensorflow-speech-recognition-challenge/data

• test and compare different network architectures (at least one of them should be Long short-term memory (LSTM))
• investigate influence of parameters change on the obtained results
• present confusion matrix (with appropriate discussion)
• in case of accuracy or efficiency problem a subset of classes can be selected and tested (e.g. only “yes” and “no” commands)
• please pay special attention on “silence” and “unknown” classes - test different approaches (e.g. separate network for their recognition)

Useful resources:

https://www.kaggle.com/davids1992/speech-representation-and-data-exploration

https://towardsdatascience.com/recognizing-speech-commands-using-recurrent-neural-networks-with-attention-c2b2ba17c837

https://www.coursera.org/lecture/nlp-sequence-models/recurrent-neural-network-model- ftkzt

https://pathmind.com/wiki/lstm