Evaluation of the Space Denoising Algorithm on AURORA2
Christophe Cerisara, Khalid Daoudi · 2006
Recently we introduced a new and simple denoising algorithm, called SPACE, that yielded promising preliminary results in noise robust speech recognition. SPACE is essentially based on GMM modeling of clean an noisy speech. In this paper, we evaluate the performance of SPACE on Aurora2 and show that they are globally not satisfactory, essentially because the Gaussian correspondence assumption is not verified. We then propose a new training procedure for the GMMs that achieves a better Gaussian correspondence. We further develop a simple adaptation algorithm to handle unknown environments that preserves the Gaussian correspondence. We evaluate the new denoising algorithm on Aurora2. The results show that it outperforms the multistyle models, sometimes significantly, on the three test sets of Aurora2