A locally weighted distance measure for example based speech recognition

State-of-the-art speech recognition relies on a state-dependent distance measure. In HMM systems, the distance measure is trained into state-dependent covariance matrices using a maximum likelihood or discriminative criterion. This "automatic" adjustment of the distance measure is traditionally considered an inherent advantage of HMMs over DTW (dynamic time warping) recognizers, as those typically rely on a uniform Euclidean distance. We show how to incorporate a non-uniform weighted distance measure into an example-based recognition system. By doing so, we manage to combine the superior segmental behaviour of DTW with the near-optimal acoustic distance measure as found in HMMs. The non-uniform distance measure enforces modifications to the k nearest neighbours search, an essential component in our large vocabulary DTW approach. We show that the complexity of our solution remains within bounds. The validity of the full approach is verified by experimental results on the resource management and TIDigits tasks.