13251
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, Andrew Y. Ng
We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, […]
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Josef Michalek, Jan Vanek
An extraction of feature-vectors from speech audio signal is a computationally intensive task. However, MFCC and PLP features remain the most popular for more than a decade. We made a GPU-accelerated implementation of the feature extraction processing. The implementation produces identical features as the reference Hidden Markov Toolkit (HTK) but in a fraction of the […]
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Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur
We describe the neural-network training framework used in the Kaldi speech recognition toolkit, which is geared towards training DNNs with large amounts of training data using multiple GPU-equipped or multi-core machines. In order to be as hardware-agnostic as possible, we needed a way to use multiple machines without generating excessive network traffic. Our method is […]
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Felix Weninger, Johannes Bergmann, Bjorn Schuller
In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA’s Computed Unified Device Architecture (CUDA). CURRENNT supports uni- and bidirectional RNNs with Long Short-Term Memory (LSTM) memory cells which overcome the vanishing gradient problem. To our knowledge, CURRENNT is the first publicly […]
Xiangang Li, Xihong Wu
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous […]
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Andrew L. Maas, Awni Y. Hannun, Christopher T. Lengerich, Peng Qi, Daniel Jurafsky, Andrew Y. Ng
Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recognition systems. Part of the promise of DNNs is their ability to represent increasingly complex functions as the number of DNN parameters increases. This paper investigates the performance of DNN-based hybrid speech recognition systems as DNN model size and training data […]
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X. Chen, Y. Wang, X. Liu, M.J.F. Gales, P. C. Woodland
Recurrent neural network language models (RNNLMs) are becoming increasingly popular for a range of applications including speech recognition. However, an important issue that limits the quantity of data, and hence their possible application areas, is the computational cost in training. A standard approach to handle this problem is to use class-based outputs, allowing systems to […]
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Simon Wiesler, Alexander Richard, Pavel Golik, Ralf Schluter, Hermann Ney
This paper describes the new release of RASR – the open source version of the well-proven speech recognition toolkit developed and used at RWTH Aachen University. The focus is put on the implementation of the NN module for training neural network acoustic models. We describe code design, configuration, and features of the NN module. The […]
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Vicente Peruffo Minotto
Given the tendency of creating interfaces between human and machines that increasingly allow simple ways of interaction, it is only natural that research effort is put into techniques that seek to simulate the most conventional mean of communication humans use: the speech. In the human auditory system, voice is automatically processed by the brain in […]
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Patrick Cardinal
The speed of processors has remained stable over the past few years. The trend may even be towards slower speeds in order to satisfy the ever increasing demands of energy efficiency. This tendency is already apparent in the area of mobile devices. In order to take full advantage of the processing power offered by modern […]
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Haofeng Kou, Weijia Shang, Ian Lane, Jike Chong
In this paper, we update our previous research for Mel-Frequency Cepstral Coefficient (MFCC) feature extraction [1] and describe the optimizations required for improving throughput on the Graphics Processing Units (GPU). We not only demonstrate that the feature extraction process is suitable for GPUs and a substantial reduction in computation time can be obtained by performing […]
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Jan Vanek, Jan Trmal, Josef V. Psutka, Josef Psutka
Gaussian mixture models (GMMs) are often used in various data processing and classification tasks to model a continuous probability density in a multi-dimensional space. In cases, where the dimension of the feature space is relatively high (e.g. in the automatic speech recognition (ASR)), GMM with a higher number of Gaussians with diagonal covariances (DC) instead […]
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