Kernel eigenspace-based MLLR adaptation (2007)
Mak, Brian Kan-Wing, Hsiao, Roger
Recently, we have been investigating the application of kernel methods for fast speaker adaptation by exploiting possible non-linearity in the input speaker space. In this paper, we propose another...
Robustness of several kernel-based fast adaptation methods on noisy LVCSR (2007)
Mak, Brian Kan-Wing, Hsiao, Roger
We have been investigating the use of kernel methods to improve conventional linear adaptation algorithms for fast adaptation, when there are less than 10s of adaptation speech. On clean speech, we...
Minimization of utterance verification error rate as a constrained optimization problem (2006)
Siu, Man Hung, Mak, Brian Kan-Wing, Au, Wing-hei
Since utterance verification (UV) may be treated as a 2-class classification problem, it may be improved with discriminative training such as minimum verification error training or minimum...
Lai, Yiu-Pong, Siu, Man Hung, Mak, Brian Kan-Wing
Traditionally, static mel-frequency cepstral coefficients (MFCCs) are derived by discrete cosine transformation (DCT), and dynamic MFCCs are derived by linear regression. Their derivation may be...
Mak, Brian Kan-Wing, Hsiao, Roger, Ho, Simon, Kwok, James Tin-Yau
Recently, we proposed an improvement to theconventional eigenvoice (EV) speaker adaptation using kernel methods. In our novel kernel eigenvoice (KEV) speaker adaptation [1], speaker supervectors are...
Mak, Brian Kan-Wing, Hsiao, Roger, Ho, Simon, Kwok, James Tin-Yau
Recently, we proposed an improvement to theconventional eigenvoice (EV) speaker adaptation using kernel methods. In our novel kernel eigenvoice (KEV) speaker adaptation [1], speaker supervectors are...
High-density discrete HMM with the use of scalar quantization indexing (2005)
Mak, Brian Kan-Wing, Au Yeung, Siu-Kei, Lai, Yiu-Pong, Siu, Man Hung
With the advance in semiconductor memory and the availability of very large speech corpora (of hundreds to thousands of hours of speech), we would like to revisit the use of discrete hidden Markov...
Hsiao, Roger, Mak, Brian Kan-Wing
Eigenvoice (EV) speaker adaptation has been shown effective for fast speaker adaptation when the amount of adaptation data is scarce. In the past two years, we have been investigating the application...
Pruning hidden Markov models with optimal brain surgeon (2005)
Mak, Brian Kan-Wing, Chan, Kin-Wah
A method of pruning hidden Markov models (HMMs) is presented. The main purpose is to find a good HMM topology for a given task with improved generalization capability. As a side effect, the resulting...
Mak, Brian Kan-Wing, Ho, Simon
Recently, we proposed two improvements to the eigenvoice (EV) speaker adaptation using kernel methods: kernel eigenvoice (KEV) speaker adaptation, and embedded kernel eigenvoice (eKEV) speaker...
Kernel eigenspace-based MLLR adaptation using multiple regression classes (2005)
Hsiao, Roger, Mak, Brian Kan-Wing
Recently, we have been investigating the application of kernel methods to improve the performance of eigenvoice-based adaptation methods by exploiting possible nonlinearity in their original working...
Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA (2004)
Mak, Brian Kan-Wing, Ho, Simon, Kwok, Tin-Yau
Recently, we proposed an improvement to the eigenvoice (EV) speaker adaptation called kernel eigenvoice (KEV) speaker adaptation. In KEV adaptation, eigenvoices are computed using kernel PCA, and a...
Improving eigenspace-based MLLR adaptation by kernel PCA (2004)
Mak, Brian Kan-Wing, Hsiao, Roger
Eigenspace-based MLLR (EMLLR) adaptation has been shown effective for fast speaker adaptation. It applies the basic idea of eigenvoice adaptation, and derives a small set of eigenmatrices using...
Towards a compact speech recognizer : subspace distribution clustering hidden Markov model (1998)
Ph.D.
Thesis, (Ph. D.)--Oregon Graduate Institute of Science and Technology, 1998.
Towards a compact speech recognizer : subspace distribution clustering hidden Markov model / (1998)
Thesis (Ph. D.)--Oregon Graduate Institute of Science and Technology, 1998.
Thesis (M.S.)--University of California, Santa Barbara, 1989.