Title :
Speaker-dependent 100 word recognition using CombNET and dynamic spectral features of speech
Author :
Kitamura, Tadashi ; Nishioka, Ken ; Iwata, A. ; Hayahara, Etsuro
Author_Institution :
Nagoya Inst. of Technol., Japan
Abstract :
Present speaker-dependent 100-word recognition using CombNET, which consists of a four-layered neural network with a comb structure, and dynamic spectral features of speech based on a two-dimensional mel-cepstrum. CombNET consists of two types of neural network. The first one is a stem network which utilizes a self-organizing algorithm and roughly classifies an input pattern. The second one consists of many branch networks using a back-propagation algorithm and precisely classifies the pattern. Experimental results on speaker-dependent word recognition for 100 Japanese city names uttered by nine male speakers show that the recognition accuracy is 97.3%
Keywords :
backpropagation; feedforward neural nets; speech recognition; CombNET; Japanese city names; back-propagation algorithm; branch networks; comb structure; dynamic spectral features; four-layered neural network; male speakers; recognition accuracy; self-organizing algorithm; speaker-dependent recognition; stem network; two-dimensional mel-cepstrum; word recognition; Backpropagation algorithms; Feedforward neural networks; Fourier transforms; Frequency domain analysis; Large-scale systems; Neural networks; Organizing; Speaker recognition; Speech recognition; Time domain analysis;
Conference_Titel :
Circuits and Systems, 1991., Proceedings of the 34th Midwest Symposium on
Conference_Location :
Monterey, CA
Print_ISBN :
0-7803-0620-1
DOI :
10.1109/MWSCAS.1991.252132
