Multi-band speech recognition using band-dependent confidence measures of blind source separation

2012 ◽  
Vol 131 (4) ◽  
pp. 3235-3235
Author(s):  
Atsushi Ando ◽  
Hiromasa Ohashi ◽  
Sunao Hara ◽  
Norihide Kitaoka ◽  
Kazuya Takeda
Keyword(s):  
Speech Recognition ◽  
Blind Source Separation ◽  
Source Separation ◽  
Confidence Measures ◽  
Multi Band

Noise-robust hands-free speech recognition using SIMO-model-based blind source separation

2007 ◽  
Author(s):  
Y. Mori ◽  
T. Takatani ◽  
H. Saruwatari ◽  
K. Shikano ◽  
T. Hiekata ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Blind Source Separation ◽  
Free Speech ◽  
Source Separation ◽  
Model Based ◽  
Noise Robust

Objective quality evaluation in blind source separation for speech recognition in a real room

2007 ◽  
Vol 87 (8) ◽  
pp. 1951-1965 ◽  
Author(s):  
Leandro Di Persia ◽  
Masuzo Yanagida ◽  
Hugo Leonardo Rufiner ◽  
Diego Milone
Keyword(s):  
Speech Recognition ◽  
Blind Source Separation ◽  
Quality Evaluation ◽  
Source Separation ◽  
Objective Quality

scholarly journals BLIND SOURCE SEPARATION (BSS) APPLIED TO SOUND IN VARIOUS CONDITIONS

2011 ◽  
Vol 14 (4) ◽  
pp. 34-42
Author(s):  
Quang Tan Truong ◽  
Huy Quang Tran ◽  
Phuong Huu Nguyen
Keyword(s):  
Speech Recognition ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Sound Sources ◽  
Sound Separation ◽  
Source Signals

Our ears often simultaneously receive various sound sources (speech, music, noise . . .), but we can still listen to the intended sound. A system of speech recognition must be able to achieve the same intelligent level. The problem is that we receive many mixed (combined) signals from many different source signals, and would like to recover them separately. This is the problem of Blind Source Separation (BSS). In the last decade or so a method has been developed to solve the above problem effectively, that is the Independent Component Analysis (ICA). There are many ICA algorithms for different applications. This report describes our application to sound separation when there are more sources than mixtures (underdetermined case). The results were quite good.

Perceptual evaluation of blind source separation for robust speech recognition

2008 ◽  
Vol 88 (10) ◽  
pp. 2578-2583 ◽  
Author(s):  
Leandro Di Persia ◽  
Diego Milone ◽  
Hugo Leonardo Rufiner ◽  
Masuzo Yanagida
Keyword(s):  
Speech Recognition ◽  
Blind Source Separation ◽  
Source Separation ◽  
Robust Speech Recognition ◽  
Perceptual Evaluation
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