Evaluation of random-projection-based feature combination on speech recognition

Random projection has been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. It represents a computationally simple method that approximately preserves the Euclidean distance of any two points through the projection. Moreover, as we are able to produce various random matrices, there may be some possibility of finding a random matrix that gives a better speech recognition accuracy among these random matrices. In this paper, we investigate the feasibility of random projection for speech feature extraction. To obtain an optimal result from among many (infinite) random matrices, a vote-based random-projection combination is introduced in this paper, where ROVER combination is applied to random-projection-based features. Its effectiveness is confirmed by word recognition experiments.