Neural Networks: Advances and Applications
Neural Networks: Advances and Applications
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
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This paper discusses a novel technique for the recognition of Assamese phonemes using Recurrent Neural Network (RNN) based phoneme recognizer. A Multi-Layer Perceptron (MLP) has been used as phoneme segmenter for the segmentation of phonemes from isolated Assamese words. Two different RNN based approaches have been considered for recognition of the phonemes and their performances have been evaluated. MFCC has been used as the feature vector for both segmentation and recognition. With RNN based phoneme recognizer, a recognition accuracy of 91% has been achieved. The RNN based phoneme recognizer has been tested for speaker mismatched and channel mismatched conditions. It has been observed that the recognizer is robust to any unseen speaker. However, its performance degrades in channel mismatch condition. Cepstral Mean Normalization (CMN) has been used to overcome the problem of performance degradation effectively.