Abstract
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).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
