scholarly journals Motion state classification for micro‐drones via modified Mel frequency cepstral coefficient and hidden Markov mode

2021 ◽  
Author(s):  
He Tian ◽  
Chunzhu Dong ◽  
Li Yuan ◽  
Hongcheng Yin
Keyword(s):  
Hidden Markov ◽  
Motion State ◽  
State Classification ◽  
Mel Frequency Cepstral Coefficient ◽  
Hidden Markov Mode

scholarly journals Simulation and detection of tamil speech accent using modified mel frequency cepstral coefficient algorithm

2018 ◽  
Vol 7 (3.3) ◽  
pp. 426
Author(s):  
Swagata Sarkar ◽  
Sanjana R ◽  
Rajalakshmi S ◽  
Harini T J
Keyword(s):  
Online Courses ◽  
Hidden Markov ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Mel Frequency Cepstral Coefficients ◽  
Propagation Algorithm ◽  
Accent Recognition ◽  
Speech Reconstruction ◽  
Mel Frequency Cepstral Coefficient

Automatic Speech reconstruction system is a topic of interest of many researchers. Since many online courses are come into the picture, so recent researchers are concentrating on speech accent recognition. Many works have been done in this field. In this paper speech accent recognition of Tamil speech from different zones of Tamilnadu is addressed. Hidden Markov Model (HMM) and Viterbi algorithms are very popularly used algorithms. Researchers have worked with Mel Frequency Cepstral Coefficients (MFCC) to identify speech as well as speech accent. In this paper speech accent features are identified by modified MFCC algorithm. The classification of features is done by back propagation algorithm.  

Research of Face Recognition Based on Wavelet Transform and EHMM

2013 ◽  
Vol 694-697 ◽  
pp. 1998-2002
Author(s):  
Xian Wei Li ◽  
Guo Long Chen
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Principal Components ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Affect Recognition ◽  
Method Of Principal Components ◽  
Critical Problems ◽  
Components Analysis ◽  
Hidden Markov Mode

A method was presented which was based on Wavelet Transform and Embedded Hidden Markov Mode (EHMM). The proposed algorithm can reduce the affections such as illuminations which affects the recognition rate using the method of Principal Components Analysis (PCA).Analyzed the critical problems that affect recognition rates in Wavelet Transform. Experimental results show that the presented method can get better results.

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