Mandarin speech recognition using convolution neural network with augmented tone features

Due to its ability of reducing spectral variations and modeling spectral correlations existed in speech signals, the convolutional neural network (CNN) has been shown effective in modeling speech compared to deep neural network (DNN). In this study, we explore applying CNN to Mandarin speech recognitions. Besides exploring appropriate CNN architecture for recognition performance, focuses are on investigating the effective acoustic features, and effectivenesses of applying tonal information which have been verified helpful in other types of acoustic models to the acoustic features in the CNN. We conduct speech recognition experiments on Mandarin broadcast speech recognition to test the effectivenesses of the proposed approaches. The CNN shows its clear superiority to the DNN, with relative reductions of character error rate (CER) among 7.7-13.1% for broadcast news speech (BN), and 5.4-9.9% for broadcast conversation speech (BC). Like in the Gaussian Mixture Model (GMM) and DNN systems, the tonal information characterized by the fundamental frequency (F₀) and fundamental frequency variations (FFV) are found still helpful in CNN models, they achieve relative CER reductions over 6.7% for BN and 4.3% for BC respectively when compared with the baseline Mel-filter bank feature.