Keyword Spotting with Long Short-term Memory Neural Network Architectures

Abstract

This paper presents the implementations of several long short-term memory neutral network architectures in keyword spotting, which are LSTM, LSTMP, BLSTM and residual LSTM. Also, LSTMP is applied in BLSTM, residual LSTM and an improved residual LSTM model, in which a spatial shortcut path connected from lower layers to the output of memory is added, is put forward in this work. These models above and DNN models are trained and compared in our experiments. The results show that the improved residual LSTMP processes the best accuracy with great efficiency. It is also presented that LSTMP brought quick convergence to various LSTM models without decrease in accuracy.