基于一维卷积神经网络的车载语音识别研究

朱锡祥; 刘凤山; 张超; 吕钊; 吴小培

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基于一维卷积神经网络的车载语音识别研究

更新时间：2026-09-04

- 基于一维卷积神经网络的车载语音识别研究
- Research on in-car Speech Recognition Based on One Dimensional Convolutional Neural Networks
- 微电子学与计算机 2017年34卷第11期页码：21-25
- 作者机构：
  
  安徽大学计算智能与信号处理教育部重点实验室机构,安徽,合肥,230039
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61271352国家自然科学基金 61401002安徽省自然科学基金 1408085QF125安徽省科技攻关強警项目 1501b04220
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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朱锡祥, 刘凤山, 张超, 吕钊, 吴小培. 基于一维卷积神经网络的车载语音识别研究[J]. 微电子学与计算机, 2017, 34(11): 21-25.

ZHU Xi-xiang, LIU Feng-shan, ZHANG Chao, et al. Research on in-car Speech Recognition Based on One Dimensional Convolutional Neural Networks[J]. 2017, 34(11): 21-25.
朱锡祥, 刘凤山, 张超, 吕钊, 吴小培. 基于一维卷积神经网络的车载语音识别研究[J]. 微电子学与计算机, 2017, 34(11): 21-25. DOI：

ZHU Xi-xiang, LIU Feng-shan, ZHANG Chao, et al. Research on in-car Speech Recognition Based on One Dimensional Convolutional Neural Networks[J]. 2017, 34(11): 21-25. DOI：

摘要

针对卷积神经网络(convolution neural networks

CNNs)是二维结构

不能很好地反映出语音信号的一维特性

因此

提出使用一维模型进行语音识别研究.其通过卷积核在时间轴上的移动

在保留频带相关性的同时可以更好的满足语音信号的时变性

进而提高识别性能.最后进行了车载语音识别对比实验

结果表明一维卷积神经网络的识别率较二维卷积神经网络提高了约10%~20%

在噪声环境下的泛化性能也明显优于后者.

Abstract

Convolution neural networks(CNNs) has been the architecture of traditional convolution neural networks is two-dimensional(2D)

which can not reflect the one-dimensional characteristic of speech signal. Therefore

a one-dimensional(1D)architecture for speech recognition was proposed

which can better satisfy the temporal variation while retaining band correlation by convolution along the time axis. Experiments of in-car speech recognition demonstrate that 1D CNNs can significantly outperform the 2D CNNs

with recognition rate improvement of 10% to 20%

and the generalization performance in noisy environment of 1D CNNs is also significantly better than the latter.

关键词

Keywords

references

Dahl G E, Yu D, Deng L, et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. Audio, Speech, and Language Processing, IEEE Transactions on, 2012, 20(1): 30-42. DOI: 30

Abdel-Hamid O, Mohamed A, Jiang H, et al. Convolutional neural networks for speech recognition[J]. Audio, Speech, and Language Processing, IEEE/ACM Transactions on, 2014, 22(10): 1533-1545. DOI: 1533

Abdel-Hamid O, Mohamed A, Jiang H, et al. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition[C]//Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. Japan, Kyoto, IEEE, 2012: 4277-4280.

Sainath T N, Kingsbury B, Saon G, et al. Deep convolutional neural networks for large-scale speech tasks[J]. Neural Networks, 2015(64): 39-48. doi: 10.1016/j.neunet.2014.08.005.

Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323 (6088): 533-536. DOI: 533

Abdel-Hamid O, Deng L, Yu D. Exploring convolutional neural network structures and optimization techniques for speech recognition [J]. Interspeech, 2013, 58 (4) : 1173-1178. DOI: 1173

Lecun Y, Bengio Y. Convolutional networks for images, speech, and time series[J]. The handbook of brain theory and neural networks, 1995, 3361(10): 1995. DOI: 1995

张晴晴, 刘勇, 潘接林, 等. 基于卷积神经网络的连续语音识别[J]. 工程科学学报, 2015, 37(9): 1212-1217. DOI: 1212

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