Hi. I'm Aldebaro Klautau. I recently joined the Federal
University of Para (UFPA) in Brazil after graduating with
my Ph.D. from University of California at San Diego.
I am part of LaPS, the Signal Processing Laboratory,
at UFPA. LaPS promotes research in digital signal
processing (DSP), which includes speech recognition, image coding, seismic
signals and DSP techniques for monitoring power systems. LaPS was created in
1993 and is one of the research laboratories of the Electrical and Computer
Engineering Department at UFPA. UFPA is the Brazilian public federal
university with the largest number of undergrad students, and is located in
Belem, a city close to the Amazon forest in North Brazil.
Three faculty members and more than 30 students (grad and
undergrad) are affiliated with the lab, which is funded by Governmental
agencies such as CNPq and CAPES, and companies such as ELETRONORTE and
CELPA. Current projects target robust speech recognition, speaker
verification based on support vector machines, DSP applied to predictive
maintenance of circuit breakers and image coding based on wavelets.
Our current plans include two things:
A good overview of MLLR and other such adaptation techniques
can be found here:
- development of maximum likelihood
linear regression (MLLR) and
maximum mutual information estimation (MMIE)
for the production system;
- hosting the ISIP summer training workshops in Brazil
starting in 2004.
The current release of the production system supports
a single transform MLLR capability often referred to
as a global mean and variance transform.
This is implemented using a function named
in a class named
This class encapsulates all our Hidden Markov modeling functionality
including accumulation of likelihoods during training.
Our singel transform MLLR implementation
has been tested and verified to give results
comparable to those published in [1-3].
Our focus will be to extend this implementation to allow
multiple transforms to be shared across acoustics models.
ECE 7000 Lecture (Jon Hamaker)
ECE 8463 Lecture (Joe Picone)
- X. Huang, A. Acero, and H.W. Hon,
Spoken Language Processing - A Guide to
Theory, Algorithm, and System Development,
Prentice Hall, Upper Saddle
River, New Jersey, USA, ISBN: 0-13-022616-5, 2001.
Our second task will be the implementation of a particular
approach to discriminative training known as MMIE.
In addition to the textbook cited above,
here is a
of discriminative approaches to HMM training. More details
will follow on this in the fall.
If you want to know more about our work or our lab,
feel free to contact me at
- C. J. Leggetter, and P. C. Woodland, "Flexible Speaker Adaptation
Using Maximum Likelihood Linear Regression,"
Proceedings of the ARPA Spoken Language Technology Workshop,
Barton Creek, 1995.
- C. J. Leggetter, Improved Acoustic Modeling for HMMs using
Linear Transformations, Ph. D. Thesis, Cambridge University, 1996.
- M. Gales and P.C. Woodland, "Variance Compensation Within the
MLLR Framework," Technical Report CUED/F-INFENT/TR242,
Cambridge University Engineering Department,