ESTIMATION OF THE PROBABILITY DENSITY-FUNCTION AND A-POSTERIORI PROBABILITY BY NEURAL NETWORKS, AND APPLICATIONS TO VOWEL RECOGNITION

Feed-forward neural networks have been used for pattern recognition, because they have an ability to estimate a posteriori probability. This paper investigates the ability to estimate the a posteriori probability by using one-dimensional Gaussian distributions, uniform distributions, their mixed distributions and real speech data, and applies the networks to speech recognition. Furthermore, the ability to estimate a probability density function of artificial data by using a vector quantization technique and neural networks and also to apply them to speech recognition also are investigated. Feed-forward neural networks, radial basis function networks (RBF), Gaussian mixed distributions and multitemplate methods for speech recognition are compared. It is concluded that the vector quantization-based RBF is the best in practice.