scholarly journals Missing Feature Theory based Interface Between Sound Source Separation and Automatic Speech Recognition and Applying to Multiple Robots

2005 ◽  
Vol 23 (6) ◽  
pp. 743-751
Author(s):  
Shunichi Yamamoto ◽  
Kazuhiro Nakadai ◽  
Hiroshi Tsujino ◽  
Hiroshi G. Okuno
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Sound Source ◽  
Source Separation ◽  
Multiple Robots ◽  
Sound Source Separation ◽  
Missing Feature Theory ◽  
Missing Feature ◽  
Feature Theory

scholarly journals Simultaneous Speech Recognition Based on Automatic Missing Feature Mask Generation by Integrating Sound Source Separation

2007 ◽  
Vol 25 (1) ◽  
pp. 92-102
Author(s):  
Shunichi Yamamoto ◽  
Jean-Marc Valin ◽  
Kazuhiro Nakadai ◽  
Mikio Nakano ◽  
Hiroshi Tsujino ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Sound Source ◽  
Source Separation ◽  
Sound Source Separation ◽  
Missing Feature

scholarly journals Noise Robust Automatic Speech Recognition Method for the Robot with Motor Noise using Missing Feature Theory

2007 ◽  
Vol 25 (8) ◽  
pp. 1189-1198
Author(s):  
Yoshitaka Nishimura ◽  
Mitsuru Ishizuka ◽  
Kazuhiro Nakadai ◽  
Mikio Nakano ◽  
Hiroshi Tsujino
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Recognition Method ◽  
Motor Noise ◽  
Noise Robust ◽  
Missing Feature Theory ◽  
Missing Feature ◽  
Feature Theory

Psychologically-Inspired Audio-Visual Speech Recognition Using Coarse Speech Recognition and Missing Feature Theory

2017 ◽  
Vol 29 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Kazuhiro Nakadai ◽  
◽  
Tomoaki Koiwa ◽  
Keyword(s):  
Speech Recognition ◽  
Real World ◽  
Visual Speech ◽  
Noise Robustness ◽  
Visual Integration ◽  
The Real ◽  
Visual Speech Recognition ◽  
Missing Feature Theory ◽  
Missing Feature ◽  
Feature Theory

[abstFig src='/00290001/10.jpg' width='300' text='System architecture of AVSR based on missing feature theory and P-V grouping' ] Audio-visual speech recognition (AVSR) is a promising approach to improving the noise robustness of speech recognition in the real world. For AVSR, the auditory and visual units are the phoneme and viseme, respectively. However, these are often misclassified in the real world because of noisy input. To solve this problem, we propose two psychologically-inspired approaches. One is audio-visual integration based on missing feature theory (MFT) to cope with missing or unreliable audio and visual features for recognition. The other is phoneme and viseme grouping based on coarse-to-fine recognition. Preliminary experiments show that these two approaches are effective for audio-visual speech recognition. Integration based on MFT with an appropriate weight improves the recognition performance by −5 dB. This is the case even in a noisy environment, in which most speech recognition systems do not work properly. Phoneme and viseme grouping further improved the AVSR performance, particularly at a low signal-to-noise ratio.**This work is an extension of our publication “Tomoaki Koiwa et al.: Coarse speech recognition by audio-visual integration based on missing feature theory, IROS 2007, pp.1751-1756, 2007.”

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