Phoneme classification and lattice rescoring based on a k-NN approach

In this paper we propose a k-NN/SASH phoneme classification algorithm that competes favourably with state-ofthe-art methods. We apply a similarity search algorithm (SASH) that has been used successfully for classification of high dimensional texts and images. Unlike other search algorithms, the computational time of SASH is not affected by the dimensionality of the data. Therefore, we generate fixed-length but high-dimensional feature vectors for phonemes using their underlying frames and those of their boundaries. The k-NN/SASH phoneme classifier is fast, efficient, and could achieve a classification rate of 79.2% for the TIMIT test database. Finally, we apply this algorithm to rescore phoneme lattices, generated by the GMMHMM monophone recognizer for both context-independent and context-dependent tasks. In both cases, the k-NN/SASH classifier leads to improvements in the recognition rate.