Keyword spotting for audiovisual archival search in Uralic languages

Publisher Copyright: © 2021 IWCLUL 2021 - 7th International Workshop on Computational Linguistics of Uralic Languages, Proceedings. All rights reserved.In this study we investigate the potential of using Automatic Speech Recognition (ASR) for keyword spotting for four Uralic languages: Finnish, Hungarian, Estonian and Komi. These languages also represent different levels on the high and low resource continuum. Although the accuracy of the ASR systems show there is a long way to go, we show that they still have potential to be useful for downstream tasks such as keyword spotting. By using a simple text search after running ASR, we are already able to achieve an F1 score of between 0.15 and 0.33, a precision of nearly 0.90 for Estonian and Hungarian, and a precision of 0.76 for Komi.Peer reviewe

The Relevance of the Source Language in Transfer Learning for ASR

This study presents new experiments on Zyrian Komi speech recognition. We use Deep-Speech to train ASR models from a language documentation corpus that contains both contemporary and archival recordings. Earlier studies have shown that transfer learning from English and using a domain matching Komi language model both improve the CER and WER. In this study we experiment with transfer learning from a more relevant source language, Russian, and including Russian text in the language model construction. The motivation for this is that Russian and Komi are contemporary contact languages, and Russian is regularly present in the corpus. We found that despite the close contact of Russian and Komi, the size of the English speech corpus yielded greater performance when used as the source language. Additionally, we can report that already an update in DeepSpeech version improved the CER by 3.9% against the earlier studies, which is an important step in the development of Komi ASR.Peer reviewe

Keyword spotting for audiovisual archival search in Uralic languages

Publisher Copyright: © 2021 IWCLUL 2021 - 7th International Workshop on Computational Linguistics of Uralic Languages, Proceedings. All rights reserved.In this study we investigate the potential of using Automatic Speech Recognition (ASR) for keyword spotting for four Uralic languages: Finnish, Hungarian, Estonian and Komi. These languages also represent different levels on the high and low resource continuum. Although the accuracy of the ASR systems show there is a long way to go, we show that they still have potential to be useful for downstream tasks such as keyword spotting. By using a simple text search after running ASR, we are already able to achieve an F1 score of between 0.15 and 0.33, a precision of nearly 0.90 for Estonian and Hungarian, and a precision of 0.76 for Komi.Peer reviewe