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Convolutional neural networks have proved very successful in image recognition, thanks to their tolerance to small translations. They have recently been applied to speech recognition as well, using a spectral representation as input. However, in this case the translations along the two axes — time and frequency — should be handled quite differently. So far, most authors have focused on convolution...
Recently, context-dependent Deep Neural Network (CD-DNN) has been found to significantly outperform Gaussian Mixture Model (GMM) for various large vocabulary continuous speech recognition tasks. Unlike the GMM approach, there is no meaningful interpretation of the DNN parameters, which makes it difficult to devise effective adaptation methods for DNNs. Furthermore, DNN parameter estimation is based...
Context-dependent deep neural network (CD-DNN) has been successfully used in large vocabulary continuous speech recognition (LVCSR). However the immense computational cost of the mini-batch based back-propagation (BP) training has become a major block to utilize massive speech data for DNN training. Previous works on BP training acceleration mainly focus on parallelization with multiple GPUs. In this...
We propose a novel deep learning vector quantization (DLVQ) algorithm based on deep neural networks (DNNs). Utilizing a strong representation power of this deep learning framework, with any vector quantization (VQ) method as an initializer, the proposed DLVQ technique is capable of learning a code-constrained codebook and thus improves over conventional VQ to be used in classification problems. Tested...
We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR). Specifically, the DNN replaces the standard Gaussian mixture model (GMM) to produce frame alignments. The use of an ASR-DNN system in the speaker recognition pipeline is attractive...
Denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltzmann machines (RBMs) in an unsupervised fashion. Then...
In our previous work, we extend the traditional stereo-based stochastic mapping by relaxing the constraint of stereo-data, which is not practical in real applications, via HMM-based speech synthesis to construct the “clean” channel data for noisy speech recognition. In this paper, we propose to use deep neural networks (DNNs) for stereo mapping compared with the joint Gaussian mixture model (GMM)...
We explore time-frequency masking to improve noise robust automatic speech recognition. Apart from its use as a frontend, we use it for providing smooth estimates of speech and noise which are then passed as additional features to a deep neural network (DNN) based acoustic model. Such a system improves performance on the Aurora-4 dataset by 10.5% (relative) compared to the previous best published...
The data sparsity problem of context-dependent (CD) acoustic modelling of deep neural networks (DNNs) in speech recognition is addressed by using the decision tree state clusters as the training targets. The CD states within a cluster cannot be distinguished during decoding. This problem, referred to as the clustering problem, is not explicitly addressed in the current literature. In our previous...
In this paper we investigate a Deep Neural Network (DNN) based approach to acoustic modeling of tonal language and assess its speech recognition performance with different features and modeling techniques. Mandarin Chinese, the most widely spoken tonal language, is chosen for testing the tone related ASR performance. Furthermore, the DNN-trained, tone-sensitive model is evaluated in automatic detection...
In this paper, a novel approach for single channel source separation (SCSS) using a deep neural network (DNN) architecture is introduced. Unlike previous studies in which DNN and other classifiers were used for classifying time-frequency bins to obtain hard masks for each source, we use the DNN to classify estimated source spectra to check for their validity during separation. In the training stage,...
Robustness of speaker recognition systems is crucial for real-world applications, which typically contain both additive noise and room reverberation. However, the combined effects of additive noise and convolutive reverberation have been rarely studied in speaker identification (SID). This paper addresses this issue in two phases. We first remove background noise through binary masking using a deep...
Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword...
Over the past few decades, a range of front-end techniques have been proposed to improve the robustness of automatic speech recognition systems against environmental distortion. While these techniques are effective for small tasks consisting of carefully designed data sets, especially when used with a classical acoustic model, there has been limited evidence that they are useful for a state-of-the-art...
In this paper, we propose a novel method to adapt context-dependent deep neural network hidden Markov model (CD-DNN-HMM) with only limited number of parameters by taking into account the underlying factors that contribute to the distorted speech signal. We derive this factorized adaptation method from the perspectives of joint factor analysis and vector Taylor series expansion, respectively. Evaluated...
We present novel bounds on the classification error which are based on the f-Divergence and, at the same time, can be used as practical training criteria. There exist virtually no studies which investigate the link between the f-Divergence, the classification error and practical training criteria. So far only the Kullback-Leibler f-Divergence has been examined in this context to formulate a bound...
Adaptation to speaker variations is an essential component of speech recognition systems. One common approach to adapting deep neural network (DNN) acoustic models is to perform global constrained maximum likelihood linear regression (CMLLR) at some point of the systems. Using CMLLR (or more generally, generative approaches) is advantageous especially in unsupervised adaptation scenarios with high...
Among many speaker adaptation embodiments, Speaker Adaptive Training (SAT) has been successfully applied to a standard Hidden-Markov-Model (HMM) speech recognizer, whose state is associated with Gaussian Mixture Models (GMMs). On the other hand, recent studies on Speaker-Independent (SI) recognizer development have reported that a new type of HMM speech recognizer, which replaces GMMs with Deep Neural...
The large number of parameters in deep neural networks (DNN) for automatic speech recognition (ASR) makes speaker adaptation very challenging. It also limits the use of speaker personalization due to the huge storage cost in large-scale deployments. In this paper we address DNN adaptation and personalization issues by presenting two methods based on the singular value decomposition (SVD). The first...
Deep neural network acoustic models have shown large improvement in performance over Gaussian mixture models (G-MMs) in recent studies. Typically, deep neural networks are trained based on the cross-entropy criterion using stochastic gradient descent (SGD). However, plain SGD requires scanning the whole training set many passes before reaching the asymptotic region, making it difficult to scale to...
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