We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
Sparsity in the eigenspace of signal covariance matrices is exploited in this paper for compression and denoising. Dimensionality reduction (DR) and quantization modules present i...
One of the difficulties in second language (L2) learning is the weakness in discriminating between acoustic diversity within an L2 phoneme category and between different categori...
Recently we have presented a novel approach for transient noise reduction that relies on non-local (NL) filtering. In this paper, we modify and extend our approach to support clu...
In this study, we propose increasing discriminative power on the maximum a posteriori (MAP)-based mapping function estimation for acoustic model adaptation. Based on the effective...