Automatic speech recognition: a study and performance evaluation on neural networks and hidden markov models

The main goal in this research is to find out possible ways to built hybrid systems, based on neural network (NN) and hidden M;arkov (HMM) models, for the task of automatic speech recognition. The investigation that has been conducted covers different types of neural network and hidden Markov models, and the combination of them into some hybrid models. The neural networks used were basically MLP and Radial Basis models. The hidden Markov models were basically different combinations of states and mixtures of the Continuous Density type of the Bakis model. A reduced set with ten words spoken in the Portuguese idiom, from Brazil, was carefully chosen to provide some pronounce and phonetic confusion. The results already obtained showed very positive, pointing toward to a high potentiality of such hybrid models

Automatic speech recognition: a comparative evaluation between neural networks and hidden markov models

In this work we do a comparative evaluation between Artificial Neural Networks (RNA's) and Continuous Hidden Markov Models (CDHMM), in the framework of the recognition of isolated words, under the constrain of using a small number of features extracted from each voice signal. In order to accomplish such comparison we used two models of neural networks: the Multilayer Perceptron (MLP) and a variant of the Radial Basis (RBF), and some HMM models. We evaluated the performance of all models using two different test set and observed that the neural models presented the best results in both cases. Seeking to improve the HMM performance we developed a hybrid system, HMM/MLP, that improved the results previously obtained with all HMMs, and even those obtained with the neural networks for the all previous HMM, and even the neural nets for the hardest test set case