A closer look on hierarchical spectro-temporal features (HIST)

Abstract

Speech recognition robust against interfering noise remains a difficult task. We previously presented a set of spectrotemporal speech features which we termed Hierarchical Spectro-Temporal (HIST) features showing improved robustness, especially when combined with RASTA-PLP. They are inspired by the receptive fields found in the mammalian auditory cortex and are organized in two hierarchical levels. A set of filters learned via ICA captures local variations and constitutes the first layer of the hierarchy. In the second layer these local variations are combined to form larger receptive fields learned via Non Negative Sparse Coding. In this paper we introduce a non-linear smoothing along the time axis of the spectrograms at the input to the hierarchy and, additionally, a more thorough performance analysis on an isolated and a continuous digit recognition task. The results show that the combination of HIST and RASTA-PLP features yields improved recognition scores in noise.