DocumentCode :
2721842
Title :
Speaker-invariant phoneme recognition using multiple neural network models
Author :
Palakal, Mathew J. ; Zoran, Michael J.
Author_Institution :
Dept. of Comput. & Inf. Sci., Purdue Univ. Sch. of Sci., Indianapolis, IN, USA
fYear :
1991
fDate :
8-14 Jul 1991
Firstpage :
839
Abstract :
The authors describe the architecture of a neural network-based ASR (automatic speech recognition) system for extracting speaker-independent features and for recognizing a special class of speech sound, such as the vowel and diphthong sounds. Speaker-invariant morphological properties that are presented in speech spectral patterns are extracted using neural networks. The system considered uses a variation of a neocognitron network model for morphological feature extraction and a perceptron model for feature classification. Some experimental performance results for the proposed system are included
Keywords :
neural nets; speech recognition; automatic speech recognition; diphthong sounds; morphological properties; multiple neural network models; neocognitron network model; perceptron model; speaker invariant phoneme recognition; vowel; Acoustic distortion; Automatic speech recognition; Feature extraction; Iron; Neural networks; Power system modeling; Radio access networks; Robustness; Spectrogram; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
Type :
conf
DOI :
10.1109/IJCNN.1991.155443
Filename :
155443
Link To Document :
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