Hierarchical bottle neck features for LVCSR
Christian Plahl, Ralf Schlüter, Hermann Ney · 2010
This paper investigates the combination of different neural network topologies for probabilistic feature extraction.On one hand, a five-layer neural network used in bottle neck feature extraction allows to obtain arbitrary feature size without dimensionality reduction by transform, independently of the training targets.On the other hand, a hierarchical processing technique is effective and robust over several conditions.Even though the hierarchical and bottle neck processing performs equally well, the combination of both topologies improves the system by 5% relative.Furthermore, the MFCC baseline system is improved by up to 20% relative.This behaviour could be confirmed on two different tasks.In addition, we analyse the influence of multi-resolution RASTA filtering and long-term spectral features as input for the neural network feature extraction.