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» Signal processing tools for speech recognition
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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...
ICASSP
2011
IEEE
12 years 11 months ago
Rapid joint speaker and noise compensation for robust speech recognition
For speech recognition, mismatches between training and testing for speaker and noise are normally handled separately. The work presented in this paper aims at jointly applying sp...
K. K. Chin, Haitian Xu, Mark J. F. Gales, Catherin...
ICASSP
2009
IEEE
14 years 2 months ago
Sparse imputation for noise robust speech recognition using soft masks
In previous work we introduced a new missing data imputation method for ASR, dubbed sparse imputation. We showed that the method is capable of maintaining good recognition accurac...
Jort F. Gemmeke, Bert Cranen
ICASSP
2009
IEEE
14 years 2 months ago
Affine invariant features and their application to speech recognition
This paper proposes a set of affine invariant features (AIFs) for sequence data. The proposed AIFs can be calculated directly from the sequence data, and their invariance to af...
Yu Qiao, Masayuki Suzuki, Nobuaki Minematsu
ICASSP
2011
IEEE
12 years 11 months ago
Bayesian sensing hidden Markov models for speech recognition
We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensin...
George Saon, Jen-Tzung Chien