Noise robust exemplar-based connected digit recognition

This paper proposes a noise robust exemplar-based speech recognition system where noisy speech is modeled as a linear combination of a set of speech and noise exemplars. The method works by finding a small number of labeled exemplars in a very large collection of speech and noise exemplars that jointly approximate the observed speech signal. We represent the exemplars using mel-energies, which allows modeling the summation of speech and noise, and estimate the activations of the exemplars by minimizing the generalized Kullback-Leibler divergence between the observations and the model. The activations of the speech exemplars are directly being used for recognition. This approach proves to be promising, achieving up to 55.8% accuracy at signal-to-noise ratio -5 dB on the AURORA-2 connected digit recognition task.