State-based labelling for a sparse representation of speech and its application to robust speech recognition

This paper proposes a state-based labeling for acoustic patterns of speech and a method for using this labelling in noiserobust automatic speech recognition. Acoustic time-frequency segments of speech, exemplars, are obtained from a training database and associated with time-varying state labels using the transcriptions. In the recognition phase, noisy speech is modeled by a sparse linear combination of noise and speech exemplars. The likelihoods of states are obtained by linear combination of the exemplar weights, which can then be used to estimate the most likely state transition path. The proposed method was tested in the connected digit recognition task with noisy speech material from the Aurora-2 database where it is shown to produce better results than the existing histogram-based labeling method.