Voice analyzing system using hidden Markov model and having plural neural network predictors

摘要

An analyzing system analyzes object signals, particularly voice signals, by estimating a generation likelihood of an observation vector sequence being a time series of feature vectors with use of a Markov model having a plurality of states and given transition probabilities from state to state. A state designation section determines a state i at a time t stochastically using the Markov model. Plural predictors, each of which is composed of a neural network and is provided per each state of the Markov model, are provided for generating a predictional vector of the feature vector x.sub.t in the state i at the time t based on values of the feature vectors other than the feature vector x.sub.t. A first calculation section calculates an error vector of the predictional vector to the feature vector x.sub.T. A second calculation section calculates a generation likelihood of the error vector using a predetermined probability distribution of the error vector according to which the error vector is generated.