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» Temporal Feature Selection for Noisy Speech Recognition
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ICMCS
2010
IEEE
164views Multimedia» more  ICMCS 2010»
13 years 6 months ago
Exploiting multimodal data fusion in robust speech recognition
This article introduces automatic speech recognition based on Electro-Magnetic Articulography (EMA). Movements of the tongue, lips, and jaw are tracked by an EMA device, which are...
Panikos Heracleous, Pierre Badin, Gérard Ba...
ICPR
2008
IEEE
14 years 7 months ago
Multiple classifier applied on predicting microsleep from speech
The aim of this study is to apply a state-of-the-art speech emotion recognition engine on the detection of microsleep endangered sleepiness states. Current approaches in speech em...
Jarek Krajewski, Anton Batliner, Rainer Wieland
ICASSP
2009
IEEE
14 years 15 days ago
An auditory-based feature for robust speech recognition
A conventional automatic speech recognizer does not perform well in the presence of noise, while human listeners are able to segregate and recognize speech in noisy conditions. We...
Yang Shao, Zhaozhang Jin, DeLiang Wang, Soundarara...
ICASSP
2010
IEEE
13 years 6 months ago
Acoustic model adaptation via Linear Spline Interpolation for robust speech recognition
We recently proposed a new algorithm to perform acoustic model adaptation to noisy environments called Linear Spline Interpolation (LSI). In this method, the nonlinear relationshi...
Michael L. Seltzer, Alex Acero, Kaustubh Kalgaonka...
ICIP
2002
IEEE
14 years 7 months ago
Application of support vector machines classifiers to visual speech recognition
In this paper we proposed a visual speech recognition network based on Support Vector Machines. Each word of the dictionary is modeled by a set of temporal sequences of visemes. E...
Mihaela Gordan, Constantine Kotropoulos, Apostolos...