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» Robust speech recognition using dynamic noise adaptation
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ICASSP
2010
IEEE
13 years 7 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...
DAGM
2008
Springer
13 years 9 months ago
Switching Linear Dynamic Models for Noise Robust In-Car Speech Recognition
Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise in the interior of a car. We compare two different Kalman fi...
Björn Schuller, Martin Wöllmer, Tobias M...
TASLP
2008
131views more  TASLP 2008»
13 years 7 months ago
Histogram-Based Quantization for Robust and/or Distributed Speech Recognition
Abstract--In a distributed speech recognition (DSR) framework, the speech features are quantized and compressed at the client and recognized at the server. However, recognition acc...
Chia-Yu Wan, Lin-Shan Lee
CSL
2007
Springer
13 years 7 months ago
On noise masking for automatic missing data speech recognition: A survey and discussion
Automatic speech recognition (ASR) has reached very high levels of performance in controlled situations. However, the performance degrades significantly when environmental noise ...
Christophe Cerisara, Sébastien Demange, Jea...
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