COSTA, W. C. A.; http://lattes.cnpq.br/1500837828145888; COSTA, Washington César de Almeida.
Resumen:
This dissertation presents a theoretical study and the software implementation of
a speech recognition system, based on Hidden Markov Models (HMM's).
HMM is a mathematical tool that makes it possible modeling of the speech sounds
in terms of a probabilistic structure. In order to do this, use is made in this work of
HMM's of the left-right type with five states and continuous fdp's, to represent the
observation vectors probability on each state of the Markov chain. The observation
vectors, which are nine-dimensional, are formed by eight cepstral coefficients and the
logarithm of the segmentai energy as the nineth parameter.
The HMM system is divided into two stages: training and classification. In the
training stage, the Baum-Welch algorithm is used to reestimate the final values of the
models. On the other hand, the classification stage makes use of the Viterbi algorithm
to provide the maximum-likelihood value between the test sentence and the reference
HMM's.
The evaluation of the proposed system is made considering two different types
of voice recognition: the independent speaker recognition and the dependent speaker
recognition. In both cases, specially on the speaker dependent mode, the avaluation
made given results really satisfactory, account to experimenting general conditions.
In addition, some important conclusions are obtained in order to provide a posterior
optimization on the proposed system.
Finally, i t is expected that this work contributes in a positive way for the motivation
of new studies on man-machine voice communication.
