155,154 research outputs found
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Attention-based encoder-decoder architectures such as Listen, Attend, and
Spell (LAS), subsume the acoustic, pronunciation and language model components
of a traditional automatic speech recognition (ASR) system into a single neural
network. In previous work, we have shown that such architectures are comparable
to state-of-theart ASR systems on dictation tasks, but it was not clear if such
architectures would be practical for more challenging tasks such as voice
search. In this work, we explore a variety of structural and optimization
improvements to our LAS model which significantly improve performance. On the
structural side, we show that word piece models can be used instead of
graphemes. We also introduce a multi-head attention architecture, which offers
improvements over the commonly-used single-head attention. On the optimization
side, we explore synchronous training, scheduled sampling, label smoothing, and
minimum word error rate optimization, which are all shown to improve accuracy.
We present results with a unidirectional LSTM encoder for streaming
recognition. On a 12, 500 hour voice search task, we find that the proposed
changes improve the WER from 9.2% to 5.6%, while the best conventional system
achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to
5% for the conventional system.Comment: ICASSP camera-ready versio
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
EM-Network: Oracle Guided Self-distillation for Sequence Learning
We introduce EM-Network, a novel self-distillation approach that effectively
leverages target information for supervised sequence-to-sequence (seq2seq)
learning. In contrast to conventional methods, it is trained with oracle
guidance, which is derived from the target sequence. Since the oracle guidance
compactly represents the target-side context that can assist the sequence model
in solving the task, the EM-Network achieves a better prediction compared to
using only the source input. To allow the sequence model to inherit the
promising capability of the EM-Network, we propose a new self-distillation
strategy, where the original sequence model can benefit from the knowledge of
the EM-Network in a one-stage manner. We conduct comprehensive experiments on
two types of seq2seq models: connectionist temporal classification (CTC) for
speech recognition and attention-based encoder-decoder (AED) for machine
translation. Experimental results demonstrate that the EM-Network significantly
advances the current state-of-the-art approaches, improving over the best prior
work on speech recognition and establishing state-of-the-art performance on
WMT'14 and IWSLT'14.Comment: ICML 202
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation
Sequence-to-Sequence (S2S) models recently started to show state-of-the-art
performance for automatic speech recognition (ASR). With these large and deep
models overfitting remains the largest problem, outweighing performance
improvements that can be obtained from better architectures. One solution to
the overfitting problem is increasing the amount of available training data and
the variety exhibited by the training data with the help of data augmentation.
In this paper we examine the influence of three data augmentation methods on
the performance of two S2S model architectures. One of the data augmentation
method comes from literature, while two other methods are our own development -
a time perturbation in the frequency domain and sub-sequence sampling. Our
experiments on Switchboard and Fisher data show state-of-the-art performance
for S2S models that are trained solely on the speech training data and do not
use additional text data.Comment: To appear in ICASSP 202
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