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.