Modelling spectro-temporal dynamics in factorisation-based noise-robust automatic speech recognition

Non-negative spectral factorisation has been used successfully for separation of speech and noise in automatic speech recognition, both in feature-enhancing front-ends and in direct classification. In this work, we propose employing spectro-temporal 2D filters to model dynamic properties of Mel-scale spectrogram patterns in addition to static magnitude features. The results are evaluated using an exemplar-based sparse classifier on the CHiME noisy speech database. After optimisation of static features and modelling of temporal dynamics with derivative features, we achieve 87.4% average score over SNRs from 9 to -6 dB, reducing the word error rate by 28.1% from our previous static-only features.