关键词: 语音识别, 鲁棒性, 深度自编码网络, GFCC特征, MFCC特征

Abstract: To solve the problem of the center and the radius determined by randomly in the speech recognition tasks based on traditional Radial Basis Function（RBF） neural network, an unsupervised pre-training method which uses a large number of unlabeled data to initialize the network parameters is proposed to replace the traditional random initialization method based on the layered mechanism of human brain on speech recognition. This paper introduces the Deep Auto-Encoder（DAE） neural network as acoustical model and further analyzes robustness of speaker-independent isolated speech recognition on small size vocabulary database. The experimental results show that DAE outperforms RBF with MFCC（Mel Frequency Cepstrum Coefficient） feature extraction. In addition, compared to MFCC, GFCC（Gammatone Frequency Cepstrum Coefficient） gives more attribution on anti-noise property with a relative accuracy improvement of 1.87% in collaborate with DAE network.

Key words: speech recognition, robustness, Deep Auto-Encoder（DAE） neural network, Gammatone Frequency Cepstrum Coefficient（GFCC）, Mel Frequency Cepstrum Coefficient（MFCC）