Title :
Spoken arabic digits recognition based on wavelet neural networks
Author :
Hu, Xiaohui ; Zhan, Lvjun ; Xue, Yun ; Zhou, Weixing ; Zhang, Liangjun
Author_Institution :
Sch. of Phys. & Telecommun., South China Normal Univ., Guangzhou, China
Abstract :
The paper describes a novel method for discrete speech recognition based on spoken Arabic digit recognition by means of wavelet neural network in which Morlet wavelet is introduced to the hidden layer. The speech signal is extracted by means of Mel Frequency Cepstral Coefficients (MFCCs) and followed by vector quantization (VQ). The experimental results obtained on a spoken Arabic digit dataset proved that it could achieve better accuracy and need less learning time than the proposed method.
Keywords :
cepstral analysis; feature extraction; natural language processing; neural nets; speech recognition; vector quantisation; wavelet transforms; MFCC; Mel frequency cepstral coefficients; Morlet wavelet; discrete speech recognition; hidden layer; speech signal extraction; spoken Arabic digit dataset; spoken Arabic digit recognition; vector quantization; wavelet neural networks; Accuracy; Biological neural networks; Feature extraction; Mel frequency cepstral coefficient; Speech; Speech recognition; Vectors; Arabic digits recognition; MFCC; VQ; wavelet neural networks;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4577-0652-3
DOI :
10.1109/ICSMC.2011.6083880
