NDLI: Unvoiced Speech Recognition Based on One-Channel Facial Myoelectric Signal

Content Provider	IEEE Xplore Digital Library
Author	Xueqin Jia Xu Wang Jinghong Li Yuyuan Du
Copyright Year	2006
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Northeastern Univ. in China, Shenyang (Xueqin Jia; Xu Wang; Jinghong Li; Yuyuan Du)
Abstract	Six Chinese vowels /a/, /o/, /e/, /i/, /u/, and /u/ are recognized based on the one-channel detected facial myoelectric signal (MES). Zygomaticus major and anterior belly of the digastric are carefully selected as the electrodes sites of MES detected. Over-sampling technology and four-layer wavelet decomposition are used to reduce noise in MES records. Digital down converter down converts the original sampling rate to Nyquist frequency. By anterior methods, clean MES is gotten with no signal distortion. For MES is not voice signal, MFCC is not selected as the feature set. According to MES characteristic, ten-order AR model is set up. The coefficients of AR model, cepstral coefficients, and amplitude of MES are chosen to form the original feature set for recognition. Principal components analysis (PCA) reduces the dimension of original feature set before the proposed BP networks. Combining two BP network classifiers, an efficient classifier is proposed. The result of experiment shows that Chinese vowel /u/, and /i/ have good classification rate (more than 90%) based on one-channel facial myoelectric signal
Starting Page	9362
Ending Page	9366
File Size	225192
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1713813
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Principal component analysis Face recognition Face detection Signal detection Facial muscles Electrodes Noise reduction Digital-to-frequency converters Sampling methods wavelet decomposition unvoice speech feature extraction BP network endpoint detection
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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