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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Lukas Machlica
The automatic speaker recognition made a significant progress in the last two decades. Huge speech corpora containing thousands of speakers recorded on several channels are at hand, and methods utilizing as much information as possible were developed. Nowadays state-of-the-art methods are based on Gaussian mixture models used to estimate relevant statistics from feature vectors extracted […]
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Jungsuk Kim
We have developed a multi-user large vocabulary speech recognition system employing a fully composed one-level weighted finite state transducer (WFST) based network on a Graphics Processing Unit (GPU). This system improves the overall throughput and latency of speech recognition engine which processes multiple users’ utterances at the same time with efficient scheduling, parameter sharing, and […]
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