Estimation of the probability density function and a posteriori probability by neural networks, and applications to vowel recognition

✍ Scribed by Seiichi Nakagawa; Yoshiyuki Ono

• Publisher: John Wiley and Sons
• Year: 1994
• Tongue: English
• Weight: 678 KB
• Volume: 25
• Category: Article
• ISSN: 0882-1666
• DOI: 10.1002/scj.4690250604

✦ Synopsis

Abstract

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.