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» Temporal Feature Selection for Noisy Speech Recognition
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INTERSPEECH
2010
14 years 4 months ago
HMM adaptation using linear spline interpolation with integrated spline parameter training for robust speech recognition
We recently proposed a method for HMM adaptation to noisy environments called Linear Spline Interpolation (LSI). LSI uses linear spline regression to model the relationship betwee...
Michael L. Seltzer, Alex Acero
ICASSP
2008
IEEE
15 years 4 months ago
Multisensor multiband cross-energy tracking for feature extraction and recognition
In this paper, we present a multisensor multiband energy tracking scheme for robust feature extraction in noisy environments. We introduce a multisensor feature extraction algorit...
Stamatios Lefkimmiatis, Petros Maragos, Athanassio...
ICMI
2004
Springer
281views Biometrics» more  ICMI 2004»
15 years 3 months ago
Articulatory features for robust visual speech recognition
Visual information has been shown to improve the performance of speech recognition systems in noisy acoustic environments. However, most audio-visual speech recognizers rely on a ...
Kate Saenko, Trevor Darrell, James R. Glass
TASLP
2008
154views more  TASLP 2008»
14 years 9 months ago
Capturing Local Variability for Speaker Normalization in Speech Recognition
The new model reduces the impact of local spectral and temporal variability by estimating a finite set of spectral and temporal warping factors which are applied to speech at the f...
Antonio Miguel, Eduardo Lleida, Richard Rose, Luis...
ICMCS
2005
IEEE
147views Multimedia» more  ICMCS 2005»
15 years 3 months ago
Comparing Feature Sets for Acted and Spontaneous Speech in View of Automatic Emotion Recognition
We present a data-mining experiment on feature selection for automatic emotion recognition. Starting from more than 1000 features derived from pitch, energy and MFCC time series, ...
Thurid Vogt, Elisabeth André