JIANG Wenbin, LIU Peilin, WEN Fei. Speech Magnitude Spectrum Reconstruction from MFCCs Using Deep Neural Network[J]. Chinese Journal of Electronics, 2018, 27(2): 393-398. doi: 10.1049/cje.2017.09.018
Citation: JIANG Wenbin, LIU Peilin, WEN Fei. Speech Magnitude Spectrum Reconstruction from MFCCs Using Deep Neural Network[J]. Chinese Journal of Electronics, 2018, 27(2): 393-398. doi: 10.1049/cje.2017.09.018

Speech Magnitude Spectrum Reconstruction from MFCCs Using Deep Neural Network

doi: 10.1049/cje.2017.09.018
Funds:  This work is supported by the National Natural Science Foundation of China (No.61401501).
  • Received Date: 2016-04-28
  • Rev Recd Date: 2017-01-06
  • Publish Date: 2018-03-10
  • This work proposes a Deep neural network (DNN) based method for reconstructing speech magnitude spectrum from Mel-frequency cepstral coefficients (MFCCs). We train a DNN using MFCC vectors as input and the corresponding speech magnitude spectrum as desired output. Exploiting the strong inference power of DNN, the proposed method has the capability to accurately estimate the speech magnitude spectrum even from truncated MFCC vectors. Experiments on TIMIT corpus demonstrate that the proposed method achieves significantly better performance compared with traditional methods.
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