20,459 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
Learning Audio Sequence Representations for Acoustic Event Classification
Acoustic Event Classification (AEC) has become a significant task for
machines to perceive the surrounding auditory scene. However, extracting
effective representations that capture the underlying characteristics of the
acoustic events is still challenging. Previous methods mainly focused on
designing the audio features in a 'hand-crafted' manner. Interestingly,
data-learnt features have been recently reported to show better performance. Up
to now, these were only considered on the frame-level. In this paper, we
propose an unsupervised learning framework to learn a vector representation of
an audio sequence for AEC. This framework consists of a Recurrent Neural
Network (RNN) encoder and a RNN decoder, which respectively transforms the
variable-length audio sequence into a fixed-length vector and reconstructs the
input sequence on the generated vector. After training the encoder-decoder, we
feed the audio sequences to the encoder and then take the learnt vectors as the
audio sequence representations. Compared with previous methods, the proposed
method can not only deal with the problem of arbitrary-lengths of audio
streams, but also learn the salient information of the sequence. Extensive
evaluation on a large-size acoustic event database is performed, and the
empirical results demonstrate that the learnt audio sequence representation
yields a significant performance improvement by a large margin compared with
other state-of-the-art hand-crafted sequence features for AEC
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