Improving automatic speech recognition using tangent distance

Abstract

In this paper we present a new approach to variance modelling in automatic speech recognition (ASR) that is based on tangent distance (TD). Using TD, classifiers can be made invariant w.r.t. small transformations of the data. Such transformations generate a manifold in a high dimensional feature space when applied to an observation vector. While conventional classifiers determine the distance between an observation and a prototype vector, TD approximates the minimum distance between their manifolds, resulting in classification that is invariant w.r.t. the underlying transformation. Recently, this approach was successfully applied in image object recognition. In this paper we describe how TD can be incorporated into ASR systems based on Gaussian mixture densities (GMD). The proposed method is embedded into a probabilistic framework. Experiments performed on the SieTill corpus for telephone line recorded German digit strings show a significant improvement in comparison with a conventional GMD approach using a comparable amount of model parameters.