Abstract
Sophisticated procedures and fast analyzing method have been needed for analyzing the stop consonants. Because the features of the stop consonants are involved in the short transitional portion between the consonant and the vowel segments. An adaptive signal processing algorithm only needs a simple procedure. Moreover, a fast converging adaptive algorithm is obtained by utilizing the ASA method which is improved by authors. The estimated parameters are converted into the critical band spectra which are useful for recognizing speech. And, the critical band spectra are converted into dynamic critical band spectra with time-varying characteristics. These spectra are inputted into a three-layered neural network for recognizing the stop consonants. In this paper, an improving method of the learning properties of the neural network is also described.
