Publication Details
The Language-Independent Bottleneck Features
Karafiát Martin, Ing., Ph.D. (DCGM FIT BUT)
Grézl František, Ing., Ph.D. (DCGM FIT BUT)
Janda Miloš, Ing. (DCGM FIT BUT)
Egorova Ekaterina, Ing., Ph.D. (DCGM FIT BUT)
Language-Independent Bottleneck Features, Multilingual Neural Network
The paper is about language-independent bottleneck features, which are generated by Multi-lingual Neural Network. This leads to features which are not biased towards any of the source languages, making the features effectively language independent.
In this paper we present novel language-independent bottleneck (BN) feature extraction framework. In our experiments we have used Multilingual Artificial Neural Network (ANN), where each language is modelled by separate output layer, while all the hidden layers jointly model the variability of all the source languages. The key idea is that the entire
ANN is trained on all the languages simultaneously, thus the BN-features are not biased towards any of the languages. Exactly for this reason, the final BN-features are considered as language independent.
In the experiments with GlobalPhone database, we show that the Multilingual BN-features consistently outperform the Monolingual BN-features. Also, the cross-lingual generalisation is evaluated, where we train on 5 source languages and test on 3 other languages. The results show that the ANN can produce very good BN-features even for unseen languages. In some cases even better than if we would train the ANN on the target language only.
@INPROCEEDINGS{FITPUB10100, author = "Karel Vesel\'{y} and Martin Karafi\'{a}t and Franti\v{s}ek Gr\'{e}zl and Milo\v{s} Janda and Ekaterina Egorova", title = "The Language-Independent Bottleneck Features", pages = "336--341", booktitle = "Proceedings of IEEE 2012 Workshop on Spoken Language Technology", year = 2012, location = "Miami, US", publisher = "IEEE Signal Processing Society", ISBN = "978-1-4673-5124-9", doi = "10.1109/SLT.2012.6424246", language = "english", url = "https://www.fit.vut.cz/research/publication/10100" }