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Title: Noise-Robust Speech Recognition Using Deep Neural Network
Authors: LI BO
Keywords: Speech Recognition, Deep Neural Network, Noise Robustness
Issue Date: 6-Jan-2014
Citation: LI BO (2014-01-06). Noise-Robust Speech Recognition Using Deep Neural Network. ScholarBank@NUS Repository.
Abstract: This thesis addresses the noise robustness of the recently developed Deep Neural Networks (DNNs) based speech recognition systems. Five techniques have been proposed. Firstly, a Mean Variance Normalization technique was developed to integrate noise statistics using Vector Taylor Series. Secondly, a Deep Split Temporal Context system was proposed to separately model sub-contexts of a long temporal span of speech. Thirdly, we revisited the missing feature theory and developed a DNN-based spectral masking system. Fourthly, an Ideal Hidden-activation Mask (IHM) was proposed to remove noise-prone latent detectors. Lastly, a noise code technique was developed to simulate IHM with reduced computational costs. Improved noise robustness has been obtained using the proposed techniques on benchmark tasks, Aurora-2 and Aurora-4. The spectral masking approach successfully ranks first in the literature on both tasks at the time of writing and is one of the most promising noise-robust techniques for DNNs.
Appears in Collections:Ph.D Theses (Open)

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