CLC number: TP391.4

On-line Access: 2019-06-10

Received: 2018-08-06

Revision Accepted: 2018-12-23

Crosschecked: 2019-05-13

Cited: 0

Clicked: 5171

Yan-min Qian, Xu Xiang. Binary neural networks for speech recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(5): 701-715.

@article{title="Binary neural networks for speech recognition",

author="Yan-min Qian, Xu Xiang",

journal="Frontiers of Information Technology & Electronic Engineering",

volume="20",

number="5",

pages="701-715",

year="2019",

publisher="Zhejiang University Press & Springer",

doi="10.1631/FITEE.1800469"

}

%0 Journal Article

%T Binary neural networks for speech recognition

%A Yan-min Qian

%A Xu Xiang

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%V 20

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%P 701-715

%@ 2095-9184

%D 2019

%I Zhejiang University Press & Springer

%DOI 10.1631/FITEE.1800469

TY - JOUR

T1 - Binary neural networks for speech recognition

A1 - Yan-min Qian

A1 - Xu Xiang

J0 - Frontiers of Information Technology & Electronic Engineering

VL - 20

IS - 5

SP - 701

EP - 715

%@ 2095-9184

Y1 - 2019

PB - Zhejiang University Press & Springer

ER -

DOI - 10.1631/FITEE.1800469

**Abstract: **Recently, deep neural networks (DNNs) significantly outperform Gaussian mixture models in acoustic modeling for speech recognition. However, the substantial increase in computational load during the inference stage makes deep models difficult to directly deploy on low-power embedded devices. To alleviate this issue, structure sparseness and low precision fixed-point quantization have been applied widely. In this work, binary neural networks for speech recognition are developed to reduce the computational cost during the inference stage. A fast implementation of binary matrix multiplication is introduced. On modern central processing unit (CPU) and graphics processing unit (GPU) architectures, a 5–7 times speedup compared with full precision floating-point matrix multiplication can be achieved in real applications. Several kinds of binary neural networks and related model optimization algorithms are developed for large vocabulary continuous speech recognition acoustic modeling. In addition, to improve the accuracy of binary models, knowledge distillation from the normal full precision floating-point model to the compressed binary model is explored. Experiments on the standard Switchboard speech recognition task show that the proposed binary neural networks can deliver 3–4 times speedup over the normal full precision deep models. With the knowledge distillation from the normal floating-point models, the binary DNNs or binary convolutional neural networks (CNNs) can restrict the word error rate (WER) degradation to within 15.0%, compared to the normal full precision floating-point DNNs or CNNs, respectively. Particularly for the binary CNN with binarization only on the convolutional layers, the WER degradation is very small and is almost negligible with the proposed approach.

**
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