2,748 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
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition
We study the problem of compressing recurrent neural networks (RNNs). In
particular, we focus on the compression of RNN acoustic models, which are
motivated by the goal of building compact and accurate speech recognition
systems which can be run efficiently on mobile devices. In this work, we
present a technique for general recurrent model compression that jointly
compresses both recurrent and non-recurrent inter-layer weight matrices. We
find that the proposed technique allows us to reduce the size of our Long
Short-Term Memory (LSTM) acoustic model to a third of its original size with
negligible loss in accuracy.Comment: Accepted in ICASSP 201
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