BUT 2014 Babel system: analysis of adaptation in NN based systems

Features based on a hierarchy of neural networks with compressive layers – Stacked Bottle-Neck (SBN) features – were recently shown to provide excellent performance in LVCSR systems. This paper summarizes several techniques investigated in our work towards Babel 2014 evaluations: (1) using several versions of fundamental frequency (F0) estimates, (2) semi-supervised training on un-transcribed data and mainly (3) adapting the NN structure at different levels. They are tested on three 2014 Babel languages with full GMMand DNN-based systems. Separately and in combination, they are shown to outperform the baselines and confirm the usefulness of bottle-neck features in current ASR systems.

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