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» Articulatory features for robust visual speech recognition
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SAC
2010
ACM
13 years 7 months ago
Visual processing-inspired fern-audio features for noise-robust speaker verification
In this paper, we consider the problem of speaker verification as a two-class object detection problem in computer vision, where the object instances are 1-D short-time spectral v...
Anindya Roy, Sébastien Marcel
TASLP
2011
13 years 2 months ago
Advances in Missing Feature Techniques for Robust Large-Vocabulary Continuous Speech Recognition
— Missing feature theory (MFT) has demonstrated great potential for improving the noise robustness in speech recognition. MFT was mostly applied in the log-spectral domain since ...
Maarten Van Segbroeck, Hugo Van Hamme
ICASSP
2011
IEEE
12 years 11 months ago
Amplitude modulation spectrogram based features for robust speech recognition in noisy and reverberant environments
In this contribution we present a feature extraction method that relies on the modulation-spectral analysis of amplitude fluctuations within sub-bands of the acoustic spectrum by ...
Niko Moritz, Jörn Anemüller, Birger Koll...
UAIS
2008
172views more  UAIS 2008»
13 years 7 months ago
Recent developments in visual sign language recognition
Abstract Research in the field of sign language recognition has made significant advances in recent years. The present achievements provide the basis for future applications with t...
Ulrich von Agris, Jörg Zieren, Ulrich Canzler...
ICASSP
2008
IEEE
14 years 1 months ago
Irrelevant variability normalization based HMM training using map estimation of feature transforms for robust speech recognition
In the past several years, we’ve been studying feature transformation (FT) approaches to robust automatic speech recognition (ASR) which can compensate for possible “distortio...
Donglai Zhu, Qiang Huo