Denoising Algorithms using Stacked RNN models for In-Car Speech Recognition System
Anirban Panda · 2018
In this paper, we have implemented Stacked Recurrent Neural Network for a Robust Speech Recognition System inside a car. To cancel out the high traffic noise, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), Bidirectional LSTM (BLSTM) and Bidirectional GRU (BGRU) have been used for mapping the noisy data with the clean speech signal. Later to increase the complexity of the model, Stacked LSTM, GRU, BLSTM and BGRU were implemented. All the models were applied to the data in Cepstral Domain and analyzed with respect to different Number of Layers and different Signal to Noise Ratio (SNR).