Publication Details
Description and Analysis of ABC Submission to NIST LRE 2022
Silnova Anna, MSc., Ph.D. (DCGM FIT BUT)
Slavíček Josef (Phonexia)
Mošner Ladislav, Ing. (DCGM FIT BUT)
Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT)
Klčo Michal, Ing. (Phonexia)
Peng Junyi (FIT BUT)
Stafylakis Themos (OMILIA)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
nguage detection, language recognition, embedding extractor, LRE, NIST
This paper summarizes our efforts in the NIST Language Recognition Evaluations 2022 resulting in systems providing competitive performance. We provide both the description and analysis of the systems. We describe what data we have used to train our models, and we follow with embedding extractors and back-end classifiers. After covering the architecture, we concentrate on post-evaluation analysis. We compare different topologies of DNN, different backend classifiers, and the impact of the data used to train them. We also report results with XLS-R pre-trained models. We present the performance of the systems in the Fixed condition, where participants are required to use only predefined data sets, and also in the Open condition allowing to use any data to train the systems.
@INPROCEEDINGS{FITPUB13111, author = "Pavel Mat\v{e}jka and Anna Silnova and Josef Slav\'{i}\v{c}ek and Ladislav Mo\v{s}ner and Old\v{r}ich Plchot and Michal Kl\v{c}o and Junyi Peng and Themos Stafylakis and Luk\'{a}\v{s} Burget", title = "Description and Analysis of ABC Submission to NIST LRE 2022", pages = "511--515", booktitle = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH", journal = "Proceedings of Interspeech - on-line", volume = 2023, number = 08, year = 2023, location = "Dublin, IE", publisher = "International Speech Communication Association", ISSN = "1990-9772", doi = "10.21437/Interspeech.2023-1529", language = "english", url = "https://www.fit.vut.cz/research/publication/13111" }