This paper presents the use of neural net hierarchy for feature extraction in ASR. The recently proposed Bottle-Neck feature extraction is extended and used in hierarchical structures to enhance the discriminative property of the features. Although many ways of hierarchical classification/feature extraction have been proposed, we restricted ourselves to use the outputs of the first stage neural network together with its inputs. This approach is evaluated on meeting speech recognition using RT'05 and RT'07 test sets. The evaluated hierarchical feature extraction brings consistent improvement over the use of just the first level neural net.
Bibliographic reference. Grézl, František / Karafiát, Martin (2010): "Hierarchical neural net architectures for feature extraction in ASR", In INTERSPEECH-2010, 1201-1204.