Fuzzy logic-based pre-classifier for tropical wood species recognition system
Machine Vision and Applications
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In this paper we investigate a non-parametric classification of English phonemes in speaker-independent continuous speech. We employ the “voting” 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 “approximate” 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 “approximate” nearest neighbour search instead of the “exact” 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 “thinning” 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.