scholarly journals Automatic alignment of phonetic events with x‐ray microbeam articulatory data and the acoustic speech signal

1989 ◽  
Vol 86 (S1) ◽  
pp. S116-S116 ◽  
Author(s):  
J. H. Greenwald ◽  
A. K. Krishnamurthy ◽  
O. Fujimura
Keyword(s):  
Speech Signal ◽  
X Ray ◽  
Automatic Alignment ◽  
Acoustic Speech Signal

Automatic alignment of a Kirkpatrick-Baez active optic by use of a soft-x-ray Hartmann wavefront sensor

2006 ◽  
Vol 31 (2) ◽  
pp. 199 ◽  
Author(s):  
Pascal Mercère ◽  
Mourad Idir ◽  
Thierry Moreno ◽  
Gilles Cauchon ◽  
Guillaume Dovillaire ◽  
...  
Keyword(s):  
Wavefront Sensor ◽  
X Ray ◽  
Automatic Alignment

Audio-Visual and Visual-Only Speech and Speaker Recognition

2009 ◽  
pp. 1-38 ◽  
Author(s):  
Derek J. Shiell ◽  
Louis H. Terry ◽  
Petar S. Aleksic ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Speaker Recognition ◽  
Speech Signal ◽  
Low Cost ◽  
Visual Speech ◽  
Future Research ◽  
User Cooperation ◽  
Acoustic Speech Signal ◽  
Recognition Systems ◽  
Acoustic Environments ◽  
Main Components

The information imbedded in the visual dynamics of speech has the potential to improve the performance of speech and speaker recognition systems. The information carried in the visual speech signal compliments the information in the acoustic speech signal, which is particularly beneficial in adverse acoustic environments. Non-invasive methods using low-cost sensors can be used to obtain acoustic and visual biometric signals, such as a person’s voice and lip movement, with little user cooperation. These types of unobtrusive biometric systems are warranted to promote widespread adoption of biometric technology in today’s society. In this chapter, the authors describe the main components and theory of audio-visual and visual-only speech and speaker recognition systems. Audio-visual corpora are described and a number of speech and speaker recognition systems are reviewed. Finally, various open issues about the system design and implementation, and present future research and development directions in this area are discussed.

Manifestations of Task‐Induced Stress in the Acoustic Speech Signal

1968 ◽  
Vol 44 (4) ◽  
pp. 993-1001 ◽  
Author(s):  
Michael H. L. Hecker ◽  
Kenneth N. Stevens ◽  
Gottfried von Bismarck ◽  
Carl E. Williams
Keyword(s):  
Speech Signal ◽  
Induced Stress ◽  
Acoustic Speech Signal

scholarly journals Automatic alignment and reconstruction of images for soft X-ray tomography

2012 ◽  
Vol 177 (2) ◽  
pp. 259-266 ◽  
Author(s):  
Dilworth Y. Parkinson ◽  
Christian Knoechel ◽  
Chao Yang ◽  
Carolyn A. Larabell ◽  
Mark A. Le Gros
Keyword(s):  
X Ray ◽  
Automatic Alignment

scholarly journals Individual Identification Through Voice Using Mel-Frequency Cepstrum Coefficient (MFCC) and Hidden Markov Models (HMM) Method

2020 ◽  
Vol 7 (1) ◽  
pp. 26
Author(s):  
Dea Sifana Ramadhina ◽  
Rita Magdalena ◽  
Sofia Saidah
Keyword(s):  
Hidden Markov Models ◽  
Speaker Recognition ◽  
Speech Signal ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Individual Identification ◽  
Acoustic Speech Signal ◽  
Mel Frequency Cepstrum Coefficient ◽  
The Voice

Voice is one of the parameters in the identification process of a person. Through the voice, information will be obtained such as gender, age, and even the identity of the speaker. Speaker recognition is a method to narrow down crimes and frauds committed by voice. So that it will minimize the occurrence of faking one's identity. The Method of Mel Frequency Cepstrum Coefficient (MFCC) can be used in the speech recognition system. The process of feature extraction of speech signal using MFCC will produce acoustic speech signal. The classification, Hidden Markov Models (HMM) is used to match unidentified speaker’s voice with the voices in database. In this research, the system is used to verify the speaker, namely 15 text dependent in Indonesian. On testing the speaker with the same as database, the highest accuracy is 99,16%.

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