Title :
A connectionist model for speaker-independent isolated word recognition
Author :
Zhu, Ming ; Fellbaum, K.
Author_Institution :
Inst. of Telecommun., Tech. Univ. of Berlin, West Germany
Abstract :
A connectionist model for speaker-independent isolated word recognition is presented. It consists of an efficient preprocessor and a network of multilayer perceptrons. The preprocessor measures the local spectral similarities by implementing a multicodebook vector quantizer and compresses nonlinearly each speech pattern into a fixed length of 24 triples. The network makes the final decision by decoding these triples which contain temporal information and minimum distortions. Preliminary experiments show a recognition rate of 95.5%, which indicates that a properly designed combination of a preprocessing scheme and a neural network can greatly reduce the computational load as well as increase recognition rates
Keywords :
decoding; neural nets; spectral analysis; speech recognition; connectionist model; decoding; multicodebook vector quantizer; multilayer perceptrons; neural nets; speaker-independent isolated word recognition; speech recognition; Computer networks; Costs; Decoding; Distortion measurement; Feature extraction; Length measurement; Multi-layer neural network; Multilayer perceptrons; Neural networks; Speech; Speech recognition;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.115766