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Proxy-word based out of vocabulary (OOV) keyword search has been proven to be quite effective in keyword search. In proxy-word based OOV keyword search, each OOV keyword is assigned several proxies and detections of the proxies are regarded as detections of the OOV keywords. However, the confidence scores of these detections are still those of the proxies from lattices. To obtain a better confidence...
Computational auditory scene analysis (CASA) system is well used in speech enhancement area in recent years. We propose a new system that combines CASA and spectral subtraction to get better enhanced speech. The CASA part consists of the latest method deep neural networks (DNNs). The original way to reconstruct the denoise signal is to use the estimated masks with direct overlap-add method ignoring...
End-to-end speech recognition systems have been successfully implemented and have become competitive replacements for hybrid systems. A common loss function to train end-to-end systems is connectionist temporal classification (CTC). This method maximizes the log likelihood between the feature sequence and the associated transcription sequence. However there are some weaknesses with CTC training. The...
At present, the issue of intrusion detection must be a hot point to all over the computer security area. In this paper, two novel intrusion detection techniques have been proposed. First, unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFS) and artificial neural networks (ANN) together, which lead to much fewer iteration numbers, higher...
This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a difference...
Speech separation based on deep neural networks (DNNs) has been widely studied recently, and has achieved considerable success. However, previous studies are mostly based on fully-connected neural networks. In order to capture the local information of speech signals, we propose to use convolutional maxout neural networks (CMNNs) to separate speech and noise by estimating the ideal ratio mask of the...
i-Vector modeling has shown to be effective for text independent speaker verification. It represents each utterance as a low-dimensional vector using factor analysis with a GMM supervector. In order to capture more complex speaker statistics, this paper proposes a new feature representation other than i-vectors for speaker verification using neural networks. In this work, stacked bottleneck features...
The Context-Dependent Deep-Neural-Network HMM, or CD-DNN-HMM, is a powerful acoustic modeling technique. Its training process typically involves unsupervised pre-training and supervised fine-tuning. In the paper, we demonstrate that the performance of DNNs can be improved by utilizing a large amount of unlabeled data in the training procedure. In our method, CD-DNN-HMM trained using 309 hours of unlabeled...
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