Integration of fixed and multiple resolution analysis in a speech recognition system

Compares the performance of an operational automatic speech recognition system when Mel frequency-scaled cepstral coefficients (MFCCs), J-Rasta perceptual linear prediction coefficients (J-Rasta PLP) and energies from a multi resolution analysis (MRA) tree of filters are used as input features to a hybrid system consisting of a neural network (NN) which provides observation probabilities for a network of hidden Markov models (HMM). Furthermore, the paper compares the performance of the system when various combinations of these features are used showing a WER reduction of 16% w.r.t. the use of J-Rasta PLP coefficients, when J-Rasta PLP coefficients are combined with the energies computed at the output of the leaves of an MRA filter tree. Such a combination is practically feasible thanks to the NN architecture used in the system. Recognition is performed without any language model on a very large test set including many speakers uttering proper names from different locations of the Italian public telephone network