车间环境下机器人语音控制的特征提取算法

doi:10.19665/j.issn1001-2400.2020.02.003

Abstract

Abstract:

In the real working environment,the mobile robots have a poor recognition performance to speech control commands due to the noise effect. Aiming at this issue,this paper proposes a new algorithm based on the gammatone frequency cepstral coefficient and the mixed feature extraction of the Teager energy operator. This algorithm replaces the common Mel filter with the Gammatone filter. In the process of extracting gammatone frequency cepstral coefficients,the Teager energy operator reflecting the energy of speech signal is added to form a new feature, with the dynamic characteristics of the speech signal considered. It is combined with the first-order difference parameters to form a mixed feature. And the principal component analysis is made to reduce the dimension,and the final mixed features are used to the speech recognition system for control command of the mobile robot. Experimental results show that,in the environment of the workshop noise and signal-to-noise ratio of 10dB,the recognition rate of mixed features is improved by 12.20% compared with the mel frequency cepstrum coefficient. The recognition rate of the mixed feature is increased by 1.02% when the dimension is reduced by principal component analysis.

Key words: Gammatone filter, Teager energy operator, feature extraction, robot control

CLC Number:

TN912.34

WANG Xiaohua,YAO Pengchao,MA Liping,WANG Wenjie,ZHANG Lei. Algorithm for extraction of features of robot speech control in the factory environment[J].Journal of Xidian University, 2020, 47(2): 16-22.

Figures/Tables 9

References 15

[1]	ISHI C T, ISHIGURO H, HAGITA N . Analysis of Head Motions and Speech, and Head Motion Control in an Android Robot[C]// Geminoid Studies: Science and Technologies for Humanlike Teleoperated Androids. Singapore: Springer Singapore, 2018: 89-110.
[2]	PIRES J N, AZAR A S . Advances in Robotics for Additive/Hybrid Manufacturing: Robot Control, Speech Interface and Path Planning[J]. Industrial Robot, 2018,45(3):311-327.
[3]	MAGASSOUBA A, BERTIN N, CHAUMETTE F . Exploiting the Distance Information of the Interaural Level Difference for Binaural Robot Motion Control[J]. IEEE Robotics and Automation Letters, 2018,3(3):2048-2055.
[4]	KUMAR A, ROUT S S, GOEL V . Speech Mel Frequency Cepstral Coefficient Feature Classification Using Multi Level Support Vector Machine[C]// Proceedings of the 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics. Piscataway: IEEE, 2017: 134-138.
[5]	SHAHIN I . Emotion Recognition Based on Third-order Circular Suprasegmental Hidden Markov Model[C]// Proceedings of the 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology. Piscataway: IEEE, 2019: 800-805.
[6]	ISHIMITSU S . Speech Recognition Method and Speech Recognition Apparatus: US6067513A[P]. 2000 -05-23.
[7]	TAZI E B . A Robust Speaker Identification System Based on the Combination of GFCC and MFCC Methods[C]// Proceedings of the 2017 International Conference on Multimedia Computing and Systems. Washington: IEEE Computer Society, 2017: 54-58.
[8]	CAN G, AKBAS C E, CETIN A E . Recognition of Vessel Acoustic Signatures Using Non-linear Teager Energy Based Features[C]// Proceedings of the 2016 International Workshop on Computational Intelligence for Multimedia Understanding. Piscataway: IEEE, 2016: 7801190.
[9]	白静, 史燕燕, 薛珮芸 , 等. 融合非线性幂函数和谱减法的CFCC特征提取[J]. 西安电子科技大学学报, 2019,46(1):86-92.
	BAI Jing, SHI Yanyan, XUE Peiyun , et al. CFCC Feature Extraction for Fusion of the Power-law Nonlinearity Function and Spectral Subtraction[J]. Journal of Xidian University, 2019,46(1):86-92.
[10]	MARKOVIĆ B, GALIĆ J, GROZDIĆ D , et al. Whispered Speech Recognition Based on Gammatone Filterbank Cepstral Coefficients[J]. Journal of Communications Technology and Electronics, 2017,62(11):1255-1261.
[11]	BANDELA S R, KUMAR T K . Stressed Speech Emotion Recognition Using Feature Fusion of Teager Energy Operator and MFCC[C]// Proceedings of the 2017 8th International Conference on Computing, Communications and Networking Technologies. Piscataway: IEEE, 2017: 8204149.
[12]	MONTEIRO R L D C, PEREIRA V, COSTA H G . Analysis of the Better Life Index trough a Cluster Algorithm[J]. Social Indicators Research, 2019,142(2):477-506.
[13]	TONG Z, SHI D, YANG S . Scene SLAM: a SLAM Framework Combined with Scene Detection[C]// Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics. Piscataway: IEEE, 2017: 1-8.
[14]	KORSHUNOV P, GONÇALVES A R, VIOLATO R P V , et al. On the Use of Convolutional Neural Networks for Speech Presentation Attack Detection[C]// Proceedings of the 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis. Piscataway: IEEE, 2018: 1-8.
[15]	张雪英, 牛溥华, 高帆 . 基于DNN-LSTM的VAD算法[J]. 清华大学学报(自然科学版), 2018,58(5):509-515.
	ZHANG Xueying, NIU Puhua, GAO Fan . VAD Algorithm Based on DNN-LSTM[J]. Journal of Tsinghua University (Science and Technology), 2018,58(5):509-515.

特征参数	识别率/%
MFCC	95.42
GFCC	93.16
TEOGFCC	94.24
ΔTEOGFCC+TEOGFCC	95.33

噪声类型	特征提取算法	信噪比/dB
噪声类型	特征提取算法	-5	0	5	10	15	20
白噪声	MFCC	35.25	41.24	62.05	71.71	80.24	85.25
	GFCC	40.25	48.35	64.25	76.25	85.65	87.75
	TEOGFCC	48.81	65.21	74.24	80.20	87.21	90.20
	TEOGFCC+ΔTEOGFCC	50.64	67.32	77.58	84.38	89.01	91.14
粉红噪声	MFCC	36.70	42.13	63.08	72.31	81.25	86.69
	GFCC	41.25	49.05	66.21	75.65	84.36	88.82
	TEOGFCC	49.25	66.32	74.06	80.35	88.31	90.05
	TEOGFCC+ΔTEOGFCC	51.88	68.24	77.86	85.63	90.32	91.78
汽车噪声	MFCC	34.24	42.25	63.58	71.12	81.33	86.96
	GFCC	43.21	50.26	68.32	76.30	85.65	87.24
	TEOGFCC	47.65	63.45	73.26	80.09	87.87	90.74
	TEOGFCC+ΔTEOGFCC	50.24	67.69	78.35	84.25	89.81	91.65
车间噪声	MFCC	35.58	43.28	65.36	73.25	82.16	85.23
	GFCC	42.32	51.25	67.48	75.91	86.51	87.24
	TEOGFCC	48.04	61.68	72.55	81.48	86.61	91.34
	TEOGFCC+ΔTEOGFCC	51.64	68.28	79.85	85.45	90.81	92.08

Algorithm for extraction of features of robot speech control in the factory environment

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