A fast phoneme recognition system based on sparse representation of test utterances

In this paper, a fast phoneme recognition system is introduced based on sparse representation. In this approach, the phoneme recognition is fulfilled by Viterbi decoding on support vector machines (SVM) output probability estimates. The candidate classes for classification are adaptively pruned by a k-dimensional (KD) tree search followed by a sparse representation (SR) based class selector with adaptive number of classes. We applied the proposed approach to introduce a phoneme recognition system and compared it with some well-known phoneme recognition systems according to accuracy and complexity issues. By this approach, we obtain competitive phoneme error rate with promising computational complexity in comparison with the state of the art phoneme recognition systems which causes this approach become a suitable candidate for automatic speech recognition (ASR) applications.