58,429 research outputs found
Workload-aware Automatic Parallelization for Multi-GPU DNN Training
Deep neural networks (DNNs) have emerged as successful solutions for variety
of artificial intelligence applications, but their very large and deep models
impose high computational requirements during training. Multi-GPU
parallelization is a popular option to accelerate demanding computations in DNN
training, but most state-of-the-art multi-GPU deep learning frameworks not only
require users to have an in-depth understanding of the implementation of the
frameworks themselves, but also apply parallelization in a straight-forward way
without optimizing GPU utilization. In this work, we propose a workload-aware
auto-parallelization framework (WAP) for DNN training, where the work is
automatically distributed to multiple GPUs based on the workload
characteristics. We evaluate WAP using TensorFlow with popular DNN benchmarks
(AlexNet and VGG-16), and show competitive training throughput compared with
the state-of-the-art frameworks, and also demonstrate that WAP automatically
optimizes GPU assignment based on the workload's compute requirements, thereby
improving energy efficiency.Comment: This paper is accepted in ICASSP201
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning
One of the challenges in Speech Emotion Recognition (SER) "in the wild" is
the large mismatch between training and test data (e.g. speakers and tasks). In
order to improve the generalisation capabilities of the emotion models, we
propose to use Multi-Task Learning (MTL) and use gender and naturalness as
auxiliary tasks in deep neural networks. This method was evaluated in
within-corpus and various cross-corpus classification experiments that simulate
conditions "in the wild". In comparison to Single-Task Learning (STL) based
state of the art methods, we found that our MTL method proposed improved
performance significantly. Particularly, models using both gender and
naturalness achieved more gains than those using either gender or naturalness
separately. This benefit was also found in the high-level representations of
the feature space, obtained from our method proposed, where discriminative
emotional clusters could be observed.Comment: Published in the proceedings of INTERSPEECH, Stockholm, September,
201
Very Deep Convolutional Neural Networks for Robust Speech Recognition
This paper describes the extension and optimization of our previous work on
very deep convolutional neural networks (CNNs) for effective recognition of
noisy speech in the Aurora 4 task. The appropriate number of convolutional
layers, the sizes of the filters, pooling operations and input feature maps are
all modified: the filter and pooling sizes are reduced and dimensions of input
feature maps are extended to allow adding more convolutional layers.
Furthermore appropriate input padding and input feature map selection
strategies are developed. In addition, an adaptation framework using joint
training of very deep CNN with auxiliary features i-vector and fMLLR features
is developed. These modifications give substantial word error rate reductions
over the standard CNN used as baseline. Finally the very deep CNN is combined
with an LSTM-RNN acoustic model and it is shown that state-level weighted log
likelihood score combination in a joint acoustic model decoding scheme is very
effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%,
further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN
joint decoding.Comment: accepted by SLT 201
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