Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67643
Title: Bottleneck features from SNR-adaptive denoising deep classifier for speaker identification
Authors: Tan, Z
Mak, MW 
Keywords: Deep belief networks
Deep learning
Bottleneck features
Denoising autoencoder
Speaker identification
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Hong Kong, China, 16-19 Dec 2015, p.1035-1040 How to cite?
Abstract: In this paper, we explore the potential of using deep learning for extracting speaker-dependent features for noise robust speaker identification. More specifically, an SNR-adaptive denoising classifier is constructed by stacking two layers of restricted Boltzmann machines (RBMs) on top of a denoising deep autoencoder, where the top-RBM layer is connected to a soft-max output layer that outputs the posterior probabilities of speakers and the top-RBM layer outputs speaker-dependent bottleneck features. Both the deep autoencoder and RBMs are trained by contrastive divergence, followed by backpropagation fine-tuning. The autoencoder aims to reconstruct the clean spectra of a noisy test utterance using the spectra of the noisy test utterance and its SNR as input. With this denoising capability, the output from the bottleneck layer of the classifier can be considered as a low-dimension representation of denoised utterances. These frame-based bottleneck features are than used to train an iVector extractor and a PLDA model for speaker identification. Experimental results based on a noisy YOHO corpus show that the bottleneck features slightly outperform the conventional MFCC under low SNR conditions and that fusion of the two features lead to further performance gain, suggesting that the two features are complementary with each other.
URI: http://hdl.handle.net/10397/67643
ISBN: 978-9-8814-7680-7 (electronic)
978-1-4673-9593-9 (print on demand(PoD))
DOI: 10.1109/APSIPA.2015.7415429
Appears in Collections:Conference Paper

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