Bottleneck ANN: dealing with small amount of data in shift-MLLR adaptation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43915991" target="_blank" >RIV/49777513:23520/12:43915991 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Bottleneck ANN: dealing with small amount of data in shift-MLLR adaptation
Original language description
The aim of this work is to propose a refinement of the shift-MLLR (shift Maximum Likelihood Linear Regression) adaptation of an acoustics model in the case of limited amount of adaptation data, which can lead to ill-conditioned transformations matrices.We try to suppress the influence of badly estimated transformation parameters utilizing the bottleneck Artificial Neural Network (ANN). The ill-conditioned shift-MLLR transformation is propagated through a bottleneck ANN (suitably trained beforehand), and the output of the net is used as the new refined transformation. To train the ANN the well and the badly conditioned shift-MLLR transformations are used as outputs and inputs of ANN, respectively.
Czech name
—
Czech description
—

Project
<a href="/en/project/TA01030476" target="_blank" >TA01030476: Intelligent technologies for improving air traffic security</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings 2012 IEEE 11th International Conference on Signal Processing
ISBN
978-1-4673-2194-5
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
507-510
Publisher name
IEEE Press
Place of publication
Beijing
Event location
Beijing
Event date
Oct 21, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)