Outlier Correction for Local Distance Measures in Example Based Speech Recognition

Example based speech recognition is critically dependent on the quality of the acoustic distance measure between input and reference vectors. In the past, the commonly used Euclidean distance has been refined to take into account the covariance of the different sounds, resulting in a class dependent distance measure. However, using the same measure for the whole class is still too crude: vectors in the tails of the distribution (outliers) are unduly considered equally representative of the class as those in the centre. In this paper, we derive two techniques inspired by non-parametric density estimation that explicitly adjust the distance measure based on the position of the reference vector in its class. Experiments on three low-level acoustic tasks show that "data sharpening" results in a substantial improvement, while "adaptive kernels" have minimal effect.