Study of Large Data Resources for Multilingual Training and System Porting

Study of Large Data Resources for Multilingual Training and System Porting

Paper in proceedings (conference paper)

This study investigates the behavior of a feature extraction neural network model trained on a large amount of single language data("source language") on a set of under-resourced target languages. The coverage of the source language acoustic space was changedin two ways: (1) by changing the amount of training data and (2) by altering the level of detail of acoustic units (by changingthe triphone clustering). We observe the effect of these changes on the performance on target language in two scenarios: (1) thesource-language NNs were used directly, (2) NNs were first ported to target language.The results show that increasing coverage as well as level of detail on the source language improves the target language systemperformance in both scenarios. For the first one, both source language characteristic have about the same effect. For the secondscenario, the amount of data in source language is more important than the level of detail.The possibility to include large data into multilingual training set was also investigated. Our experiments point out possiblerisk of over-weighting the NNs towards the source language with large data. This degrades the performance on part of the targetlanguages, compared to the setting where the amounts of data per language are balanced.

This study investigates the behavior of a feature extraction neural network model trained on a large amount of single language data("source language") on a set of under-resourced target languages. The coverage of the source language acoustic space was changedin two ways: (1) by changing the amount of training data and (2) by altering the level of detail of acoustic units (by changingthe triphone clustering). We observe the effect of these changes on the performance on target language in two scenarios: (1) thesource-language NNs were used directly, (2) NNs were first ported to target language.The results show that increasing coverage as well as level of detail on the source language improves the target language systemperformance in both scenarios. For the first one, both source language characteristic have about the same effect. For the secondscenario, the amount of data in source language is more important than the level of detail.The possibility to include large data into multilingual training set was also investigated. Our experiments point out possiblerisk of over-weighting the NNs towards the source language with large data. This degrades the performance on part of the targetlanguages, compared to the setting where the amounts of data per language are balanced.

Stacked Bottle-Neck; feature extraction; multilingual training; large data; Fisher database

Stacked Bottle-Neck; feature extraction; multilingual training; large data; Fisher database

GRÉZL, F.; EGOROVA, E.; KARAFIÁT, M.

2018

09.05.2016

Elsevier Science

Yogyakarta

Procedia Computer Science

1877-0509

Procedia Computer Science

2016

81

Kingdom of the Netherlands

15

22

8

http://www.sciencedirect.com/science/article/pii/S1877050916300382

grezl_sltu2016_08-8028