Detection-based speech recognition with sparse point process models

We present a bottom-up approach to connected digit recognition in which (i) the speech signal is transformed into a sparse set of acoustic events in time, (ii) point process models (PPM) of these events are used to detect candidate digit occurrences, and (iii) the candidate digit detections are reduced to a single digit sequence prediction by using a previously proposed graph-based optimization. We find the performance of this detection-based system on the AURORA2 evaluation matches that of an HTK baseline in clean speech and provides improved robustness to non-stationary noise. A similar robustness to stationary noise sources is achieved with unsupervised PPM adaptation using small amounts of the noisy data.