Publikace
Detail publikace
Citace
p. 507-510, Beijing, 2012. : Bottleneck ANN: dealing with small amount of data in shift-MLLR adaptation . IEEE 11th International Conference on Signal Processing,
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Abstrakt
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.
Abstrakt v češtině
Článek popisuje řešení nedostatečného množství dat pro shift-MLLR adaptaci pomocí neuonové sítě bottelneck.
Detail publikace
Název: | Bottleneck ANN: dealing with small amount of data in shift-MLLR adaptation |
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Autor: | Zbyněk Zajíc ; Machlica Lukáš ; Müller Luděk |
Název - česky: | Bottleneck ANN: shift-MLLR adaptace pro malé množství dat |
Jazyk publikace: | anglicky |
Rok vydání: | 2012 |
Typ publikace: | Stať ve sborníku |
Název časopisu / knihy: | IEEE 11th International Conference on Signal Processing |
Strana: | 507 - 510 |
Místo vydání: | Beijing |
Klíčová slova
ASR, Adaptation, shift-MLLR, ANN, bottleneck
Klíčová slova v češtině
ASR, Adaptace, shift-MLLR, ANN, bottleneck
BibTeX
@MISC{ZbynekZajic_2012_BottleneckANN, author = {Zbyn\v{e}k Zaj\'{i}c and Machlica Luk\'{a}\v{s} and M\"{u}ller Lud\v{e}k}, title = {Bottleneck ANN: dealing with small amount of data in shift-MLLR adaptation}, year = {2012}, journal = {IEEE 11th International Conference on Signal Processing}, address = {Beijing}, pages = {507-510}, url = {http://www.kky.zcu.cz/en/publications/ZbynekZajic_2012_BottleneckANN}, }