NDLI: A Robust Acoustic Feature Extraction Approach Based on Stacked Denoising Autoencoder

Content Provider	IEEE Xplore Digital Library
Author	Liu, J.H. Zheng, W.Q. Zou, Y.X.
Copyright Year	2015
Description	Author affiliation: Sch. of Electron. & Comput. Eng., Peking Univ., Shenzhen, China (Liu, J.H.; Zheng, W.Q.; Zou, Y.X.)
Abstract	Acoustic feature extraction (AFE) is considered as one of the most challenging techniques for speech applications since the adverse environment noises always cause significant variation on the extracted acoustic features. In this paper, we propose a systematical AFE approach which based on stacked denoising auto encoder (SDAE) aiming at extracting acoustic features automatically. Denoising auto encoder (DAE), which is trained to reconstruct a clean "repaired" input from a corrupted version of it, works as the basic building block to form SDAE. Besides, the training set with clean and noisy speech ensures the SDAE has much powerful ability to extract the robust features under different noise conditions. Considering the speaker classification task using features extracted by the proposed approach for evaluation, intensive experiments have been conducted on TIMIT and NIST SRE 2004 to show SDAE with 3 hidden layers (3L-SDAE) gives better performance than shallow layers. The results also show that the features extracted by 3L-SDAE performs better than MFCC features when SNR is lower than 6dB and act more robustly when SNR decreases. What's more, for different types of noises at SNR of 0dB, the accuracy of speaker classification using 3L-SDAE features is higher than about 84% while MFCC features is lower than 77%.
Starting Page	124
Ending Page	127
File Size	335625
Page Count	4
File Format	PDF
e-ISBN	9781479986880
DOI	10.1109/BigMM.2015.46
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stacked denoising autoencoder Noisy environment Feature extraction Speech Robust acoustic feature extraction Mel frequency cepstral coefficient Spectrogram Speaker classification Signal to noise ratio
Content Type	Text
Resource Type	Article

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