TY - GEN
T1 - Specific two words lexical semantic recognition based on the wavelet transform of narrowband spectrogram
AU - Zhang, Ling
AU - Wei, Ying
AU - Wang, Shuangwei
AU - Pan, Di
AU - Liang, Shili
AU - Xu, Tingfa
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - This paper presents a method based on wavelet transform of the narrowband spectrogram for specific two words Chinese lexical recognition. In the process of image feature extraction, the image processing technique is applied to the speech recognition field. Firstly, two-dimensional discrete db4 wavelet is used to decompose the narrowband spectrogram, which is divided into 6 layers of wavelet package decomposition, and calculates the approximate energy value. Then, the extracted approximate energy value is divided into level detail energy value, vertical detail energy and diagonal detail energy value, sets respectively as the narrowband spectrogram of the first characteristic set, the second and third feature set. The above three feature sets are used as feature vectors to support vector machine as a classifier for the overall recognition of two words Chinese vocabulary. 1000 voice samples are used in the simulation experiment. The results show that this method correct recognition rate can reach 96 percent.
AB - This paper presents a method based on wavelet transform of the narrowband spectrogram for specific two words Chinese lexical recognition. In the process of image feature extraction, the image processing technique is applied to the speech recognition field. Firstly, two-dimensional discrete db4 wavelet is used to decompose the narrowband spectrogram, which is divided into 6 layers of wavelet package decomposition, and calculates the approximate energy value. Then, the extracted approximate energy value is divided into level detail energy value, vertical detail energy and diagonal detail energy value, sets respectively as the narrowband spectrogram of the first characteristic set, the second and third feature set. The above three feature sets are used as feature vectors to support vector machine as a classifier for the overall recognition of two words Chinese vocabulary. 1000 voice samples are used in the simulation experiment. The results show that this method correct recognition rate can reach 96 percent.
KW - Spectrogram
KW - Speech recognition
KW - Support Vector Machine(SVM)
KW - db4 wavelet
UR - http://www.scopus.com/inward/record.url?scp=85048774131&partnerID=8YFLogxK
U2 - 10.1109/EIIS.2017.8298735
DO - 10.1109/EIIS.2017.8298735
M3 - Conference contribution
AN - SCOPUS:85048774131
T3 - 1st International Conference on Electronics Instrumentation and Information Systems, EIIS 2017
SP - 1
EP - 6
BT - 1st International Conference on Electronics Instrumentation and Information Systems, EIIS 2017
A2 - Li, Jun-Bao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Electronics Instrumentation and Information Systems, EIIS 2017
Y2 - 3 June 2017 through 5 June 2017
ER -
