Articulatory feature classification using surface electromyography

被引：0

作者：

Jou, Szu-Chen ^{[1
]}

Maier-Hein, Lena ^{[1
]}

Schultz, Tanja ^{[1
]}

Waibel, Alex ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Int Ctr Adv Commun Technol, Pittsburgh, PA 15213 USA

来源：

2006 IEEE International Conference on Acoustics, Speech and Signal Processing, Vols 1-13 | 2006年

关键词：

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this paper, we present an approach for articulatory feature classification based on surface electromyographic signals generated by the facial muscles. With parallel recorded audible speech and electromyographic signals, experiments are conducted to show the anticipatory behavior of electromyographic signals with respect to speech signals. On average, we found that the signals to be time delayed by 0.02 to 0.12 second. Furthermore, it is shown that different articulators have different anticipatory behavior. With offset-aligned signals, we improved the average F-score of the articulatory feature classifiers in our baseline system from 0.467 to 0.502.

引用

页码：605 / 608

页数：4

共 50 条

[1] Articulatory Feature Classification Using Nearest Neighbors
Naess, Arild Brandrud
Livescu, Karen
Prabhavalkar, Rohit
12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5, 2011, : 2312 - +
[2] Articulatory Feature Classification Using Convolutional Neural Networks
Merkx, Danny
Scharenborg, Odette
19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES, 2018, : 2142 - 2146
[3] Classification of surface electromyography patterns by using HMM
Luo, Zhizeng
Yan, Tingfang
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2008, 36 (04): : 72 - 75
[4] Using Temporal Information for Improving Articulatory-Acoustic Feature Classification
Schuppler, Barbara
van Doremalen, Joost
Scharenborg, Odette
Cranen, Bert
Boves, Lou
2009 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION & UNDERSTANDING (ASRU 2009), 2009, : 70 - 75
[5] Uterine electromyography signal feature extraction and classification
Lu, Nan
Wang, Jihong
McDermott, Isobel
Thornton, Steve
Vatish, Manu
Randeva, Harpal
International Journal of Modelling, Identification and Control, 2009, 6 (02) : 136 - 146
[6] Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants
Gomez-Vilda, Pedro
Gomez-Rodellar, Andres
Ferrandez Vicente, Jose M.
Mekyska, Jiri
Palacios-Alonso, Daniel
Rodellar-Biarge, Victoria
Alvarez-Marquina, Agustin
Eliasova, Ilona
Kostalova, Milena
Rektorova, Irena
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2019, 29 (02)
[7] Classification of Muscle Fatigue using Surface Electromyography Signals and Multifractals
Marri, Kiran
Swaminathan, Ramakrishnan
2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2015, : 669 - 674
[8] Feature selection of mime speech recognition using surface electromyography data
Zhang, Ming
Zhang, Wei
Zhang, Bixuan
Wang, You
Li, Guang
2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 3173 - 3178
[9] Ant colony optimization-based feature selection method for surface electromyography signals classification
Huang, Hu
Xie, Hong-Bo
Guo, Jing-Yi
Chen, Hui-Juan
COMPUTERS IN BIOLOGY AND MEDICINE, 2012, 42 (01) : 30 - 38
[10] Surface Electromyography Classification Method Based on Temporal Two-Dimensionalization and Convolution Feature Fusion
Luo J.
Wang W.
Wang Z.
Liu H.
Wang, Wanliang (wwl@zjut.edu.cn), 1600, Science Press (33): : 588 - 599

← 1 2 3 4 5 →