Context-independent phoneme recognition using a K-Nearest Neighbour classification approach

In this paper we investigate a non-parametric classification of English phonemes in speaker-independent continuous speech. We employ the ldquovotingrdquo k-nearest neighbour (k-NN) classifier, a powerful technique in pattern recognition problems, along with a new representation of phonemes for the speech recognition task. We also exploit the idea behind ldquoapproximaterdquo k-NN that results in a very fast way of computing the k approximate closest neighbours of each data point. Comparing the recognition performance of the proposed method with the HMM-based recognizer of HTK toolkit reveals that the k-NN-based recognizer outperforms its counterpart. In addition, incorporating the ldquoapproximaterdquo nearest neighbour search instead of the ldquoexactrdquo one results in completing the training step much faster than the HMM-based system, and the testing step with a comparable computational time. We also reduced the amount of the training data by applying a pattern recognition technique, called ldquothinningrdquo algorithm. The outcome was a considerable reduction in the k-NN search space and hence the execution time, and also a slight increase in the recognition performance.