An Innovate Approach on Biometric and Speech Recognition using Recurrent Neural Networks (RNN)

The main objective of this research paper is the use of recurrent neural networks (RNNs) to arise a productive solution to problems in authentication with electrocardiogram (ECG)-based biometrics and Speech Recognition.This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation.A recurrent neural network (RNN) is a class of Artificial Neural Networks where connections between nodes form a directed Graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected Handwriting and Speech Recognition. Recurrent neural networks (RNNs) are a powerful model for sequential data. Recurrent Neural Networks were designed to work with sequence prediction problems. Sequence prediction problems come in many forms and are best described by the types of inputs and outputs supported.Different RNN architectures with various parameter settings were evaluated, including traditional, long shortterm memory (LSTM), gated recurrent unit (GRU), unidirectional, and bidirectional networks. Unlike many existing methods, the RNN-based method does not require any feature extraction.