A robust BFCC feature extraction for ASR system
Ta-Wen Kuan, An‐Chao Tsai, Po-Hsun Sung, Jhing-Fa Wang, Hsien-Shun Kuo · Artificial Intelligence Research · 2016
An auditory-based feature extraction algorithm naming the Basilar-membrane Frequency-band Cepstral Coefficient (BFCC) is proposed to increase the robustness for automatic speech recognition. Compared to Fourier spectrogram based of the Mel-Frequency Cepstral Coefficient (MFCC) method, the proposed BFCC method engages an auditory spectrogram based on agammachirp wavelet transform to simulate the auditory response of human inner ear to improve the noise immunity. In addition, the Hidden Markov Model (HMM) is used for evaluating the proposed BFCC in phases of training and testing purposes conducted by AURORA-2 corpus with different Signal-to-Noise Ratios (SNRs) degrees of datasets. The experimental results indicate the proposed BFCC, compared with MFCC, Gammatone Wavelet Cepstral Coefficient (GWCC), and Gammatone Frequency Cepstral Coefficient (GFCC), improves the speech recognition rate by 13%, 17%, and 0.5% respectively, on average given speech samples with SNRs ranging from -5 to 20 dB.