A novel system for effective speech recognition based on artificial neural network and opposition artificial bee colony algorithm

The problem related to speech recognition system becomes challenging if vocabularies are having too many similar-sounding words. To overcome these types of challenges, an effective speech recognition system using artificial neural network (ANN) with optimization technique is proposed. In this system, distinct words spoken by different people are considered as input speech signal. The features of these input speech signals are extracted using amplitude modulation spectrogram. The extracted features are then the input to the ANN for training. The trained ANN inputs are used for predicting the isolated words during testing. In this work, the default structure of ANN is redesigned using Levenberg-Marquardt algorithm, to retrieve optimal prediction rate with accuracy. The hidden layers and neurons of the hidden layers are further optimized using the opposition artificial bee colony optimization technique. The outcome of the system demonstrates that the sensitivity, specificity, and accuracy of the proposed technique is 90.41%, 99.66%, and 99.36%, respectively, which is better than all the existing methods.