1,928 research outputs found
Improving speech recognition by revising gated recurrent units
Speech recognition is largely taking advantage of deep learning, showing that
substantial benefits can be obtained by modern Recurrent Neural Networks
(RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which
typically reach state-of-the-art performance in many tasks thanks to their
ability to learn long-term dependencies and robustness to vanishing gradients.
Nevertheless, LSTMs have a rather complex design with three multiplicative
gates, that might impair their efficient implementation. An attempt to simplify
LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just
two multiplicative gates.
This paper builds on these efforts by further revising GRUs and proposing a
simplified architecture potentially more suitable for speech recognition. The
contribution of this work is two-fold. First, we suggest to remove the reset
gate in the GRU design, resulting in a more efficient single-gate architecture.
Second, we propose to replace tanh with ReLU activations in the state update
equations. Results show that, in our implementation, the revised architecture
reduces the per-epoch training time with more than 30% and consistently
improves recognition performance across different tasks, input features, and
noisy conditions when compared to a standard GRU
English Conversational Telephone Speech Recognition by Humans and Machines
One of the most difficult speech recognition tasks is accurate recognition of
human to human communication. Advances in deep learning over the last few years
have produced major speech recognition improvements on the representative
Switchboard conversational corpus. Word error rates that just a few years ago
were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now
believed to be within striking range of human performance. This then raises two
issues - what IS human performance, and how far down can we still drive speech
recognition error rates? A recent paper by Microsoft suggests that we have
already achieved human performance. In trying to verify this statement, we
performed an independent set of human performance measurements on two
conversational tasks and found that human performance may be considerably
better than what was earlier reported, giving the community a significantly
harder goal to achieve. We also report on our own efforts in this area,
presenting a set of acoustic and language modeling techniques that lowered the
word error rate of our own English conversational telephone LVCSR system to the
level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000
evaluation, which - at least at the writing of this paper - is a new
performance milestone (albeit not at what we measure to be human performance!).
On the acoustic side, we use a score fusion of three models: one LSTM with
multiple feature inputs, a second LSTM trained with speaker-adversarial
multi-task learning and a third residual net (ResNet) with 25 convolutional
layers and time-dilated convolutions. On the language modeling side, we use
word and character LSTMs and convolutional WaveNet-style language models
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
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