Performance Analysis of SOFM based Reduced Complexity Feature Extraction Methods with back Propagation Neural Network for Multilingual Digit Recognition

Background: Speech recognition is an active area of research, used to transliterate words vocalized by individuals in order to make them machine recognizable. For real time speech recognition applications the response time, size of training data and recognition accuracy are the important aspects. Methods: A Hybrid speech recognition system is proposed on the basis on Artificial Neural Network (ANN) in this research. The Self Organising Feature Map (SOFM) is used to reduce the feature vector dimensions which are extracted using the Mel-Frequency Cepstrum Coefficients (MFCC), Perceptual Linear Predictive (PLP) and Discrete Wavelet Transform (DWT) methods. The Back Propagation Network (BPN) algorithm is used for training the Artificial Neural Network for pattern classification. Findings: The proposed method is tested with TIDIGITS data. Results indicate that despite of the large reduction in the feature vector dimensions the recognition accuracy obtained using SOFM technique is same as that of the recognition accuracy of the conventional methods. The response time is also fast and the data size of the input data is reduced considerably. The proposed hybrid system is further tested using multilingual isolated digit data.