文件名称:Feature-Denoising
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joint sparse representation
(JSR)方法用于车内语音增强的特征降噪算法-address reducing the mismatch between training
and testing conditions for hands-free in-car speech recognition. It
is well known that the distortions caused by background noise,
channel effects, etc., are highly nonlinear in the log-spectral or cepstral
domain. This letter introduces a joint sparse representation
(JSR) to estimate the underlying clean feature vector a noisy
feature vector. Performing a joint dictionary learning by sharing
the same representation coefficients, the proposed method intends
to capture the complex relationships (or mapping functions) between
clean and noisy speech. Speech recognition experiments on
realistic in-car data demonstrate that the proposed method shows
excellent recognition performance with a relative improvement of
39.4 compared with the “baseline” frontends.
(JSR)方法用于车内语音增强的特征降噪算法-address reducing the mismatch between training
and testing conditions for hands-free in-car speech recognition. It
is well known that the distortions caused by background noise,
channel effects, etc., are highly nonlinear in the log-spectral or cepstral
domain. This letter introduces a joint sparse representation
(JSR) to estimate the underlying clean feature vector a noisy
feature vector. Performing a joint dictionary learning by sharing
the same representation coefficients, the proposed method intends
to capture the complex relationships (or mapping functions) between
clean and noisy speech. Speech recognition experiments on
realistic in-car data demonstrate that the proposed method shows
excellent recognition performance with a relative improvement of
39.4 compared with the “baseline” frontends.
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Feature Denoising.pdf