Speech Recognition Using Principal Components Analysis and Neural Networks

In this paper, we intend to introduce a new approach to recognize discrete speeches, specifically pre-assumed words. Our approach is mainly based on Principal Components Analysis (PCA) and Neural Networks (NN). To do so, initially we build a data base which is provided by 20 speakers who uttered each predefined word 5 times and overall 10 Persian words. Then we apply Voice Activity Detection (VAD) and eliminate the useless portions of each frame and then by computing Mel Frequency Cepstral Coefficients (MFCCs), which are our useful features in the recognition process, and then applying PCA to reduce the size of our data set, we will successfully provide the inputs of the NN block. Using PCA will enable us to provide inputs with lower size to our recognition system which is an important feature of our approach by speeding up the training procedure while keeping the accuracy as high as possible. In another words, PCA will decrease the amount of computations we have to deal with usually in most recognition systems. We use 90% of our data set to train our algorithm and the remained 10% to test our algorithm and measure the accuracy of recognition process.