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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
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex
systems, recent studies have explored end-to-end speech recognition systems
based on neural networks, which can be trained to directly predict text from
input acoustic features. Although such systems are conceptually elegant and
simpler than traditional systems, it is less obvious how to interpret the
trained models. In this work, we analyze the speech representations learned by
a deep end-to-end model that is based on convolutional and recurrent layers,
and trained with a connectionist temporal classification (CTC) loss. We use a
pre-trained model to generate frame-level features which are given to a
classifier that is trained on frame classification into phones. We evaluate
representations from different layers of the deep model and compare their
quality for predicting phone labels. Our experiments shed light on important
aspects of the end-to-end model such as layer depth, model complexity, and
other design choices.Comment: NIPS 201
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