13 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
Light Gated Recurrent Units for Speech Recognition
A field that has directly benefited from the recent advances in deep learning
is Automatic Speech Recognition (ASR). Despite the great achievements of the
past decades, however, a natural and robust human-machine speech interaction
still appears to be out of reach, especially in challenging environments
characterized by significant noise and reverberation. To improve robustness,
modern speech recognizers often employ acoustic models based on Recurrent
Neural Networks (RNNs), that are naturally able to exploit large time contexts
and long-term speech modulations. It is thus of great interest to continue the
study of proper techniques for improving the effectiveness of RNNs in
processing speech signals.
In this paper, we revise one of the most popular RNN models, namely Gated
Recurrent Units (GRUs), and propose a simplified architecture that turned out
to be very effective for ASR. The contribution of this work is two-fold: First,
we analyze the role played by the reset gate, showing that a significant
redundancy with the update gate occurs. As a result, we propose to remove the
former from the GRU design, leading to a more efficient and compact single-gate
model. Second, we propose to replace hyperbolic tangent with ReLU activations.
This variation couples well with batch normalization and could help the model
learn long-term dependencies without numerical issues.
Results show that the proposed architecture, called Light GRU (Li-GRU), not
only reduces the per-epoch training time by more than 30% over a standard GRU,
but also consistently improves the recognition accuracy across different tasks,
input features, noisy conditions, as well as across different ASR paradigms,
ranging from standard DNN-HMM speech recognizers to end-to-end CTC models.Comment: Copyright 2018 IEE
End-to-End Attention-based Large Vocabulary Speech Recognition
Many of the current state-of-the-art Large Vocabulary Continuous Speech
Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov
Models (HMMs). Most of these systems contain separate components that deal with
the acoustic modelling, language modelling and sequence decoding. We
investigate a more direct approach in which the HMM is replaced with a
Recurrent Neural Network (RNN) that performs sequence prediction directly at
the character level. Alignment between the input features and the desired
character sequence is learned automatically by an attention mechanism built
into the RNN. For each predicted character, the attention mechanism scans the
input sequence and chooses relevant frames. We propose two methods to speed up
this operation: limiting the scan to a subset of most promising frames and
pooling over time the information contained in neighboring frames, thereby
reducing source sequence length. Integrating an n-gram language model into the
decoding process yields recognition accuracies similar to other HMM-free
RNN-based approaches