@inproceedings{dargis-etal-2024-balsutalka,
title = "{B}alsu{T}alka.lv - Boosting the Common Voice Corpus for Low-Resource Languages",
author = "Dargis, Roberts and
Znotins, Arturs and
Auzina, Ilze and
Saulite, Baiba and
Reinsone, Sanita and
Dejus, Raivis and
Klavinska, Antra and
Gruzitis, Normunds",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.187",
pages = "2080--2085",
abstract = "Open speech corpora of substantial size are seldom available for less-spoken languages, and this was recently the case also for Latvian with its 1.5M native speakers. While there exist several closed Latvian speech corpora of 100+ hours, used to train competitive models for automatic speech recognition (ASR), there were only a few tiny open datasets available at the beginning of 2023, the 18-hour Latvian Common Voice 13.0 dataset being the largest one. In the result of a successful national crowdsourcing initiative, organised jointly by several institutions, the size and speaker diversity of the Latvian Common Voice 17.0 release have increased more than tenfold in less than a year. A successful follow-up initiative was also launched for Latgalian, which has been recognized as an endangered historic variant of Latvian with 150k speakers. The goal of these initiatives is not only to enlarge the datasets but also to make them more diverse in terms of speakers and accents, text genres and styles, intonations, grammar and lexicon. They have already become considerable language resources for both improving ASR and conducting linguistic research. Since we use the Mozilla Common Voice platform to record and validate speech samples, this paper focuses on (i) the selection of text snippets to enrich the language data and to stimulate various intonations, (ii) an indicative evaluation of the acquired corpus and the first ASR models fine-tuned on this data, (iii) our social campaigns to boost and maintain this initiative.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dargis-etal-2024-balsutalka">
<titleInfo>
<title>BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Roberts</namePart>
<namePart type="family">Dargis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arturs</namePart>
<namePart type="family">Znotins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ilze</namePart>
<namePart type="family">Auzina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Baiba</namePart>
<namePart type="family">Saulite</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sanita</namePart>
<namePart type="family">Reinsone</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Raivis</namePart>
<namePart type="family">Dejus</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antra</namePart>
<namePart type="family">Klavinska</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Normunds</namePart>
<namePart type="family">Gruzitis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Open speech corpora of substantial size are seldom available for less-spoken languages, and this was recently the case also for Latvian with its 1.5M native speakers. While there exist several closed Latvian speech corpora of 100+ hours, used to train competitive models for automatic speech recognition (ASR), there were only a few tiny open datasets available at the beginning of 2023, the 18-hour Latvian Common Voice 13.0 dataset being the largest one. In the result of a successful national crowdsourcing initiative, organised jointly by several institutions, the size and speaker diversity of the Latvian Common Voice 17.0 release have increased more than tenfold in less than a year. A successful follow-up initiative was also launched for Latgalian, which has been recognized as an endangered historic variant of Latvian with 150k speakers. The goal of these initiatives is not only to enlarge the datasets but also to make them more diverse in terms of speakers and accents, text genres and styles, intonations, grammar and lexicon. They have already become considerable language resources for both improving ASR and conducting linguistic research. Since we use the Mozilla Common Voice platform to record and validate speech samples, this paper focuses on (i) the selection of text snippets to enrich the language data and to stimulate various intonations, (ii) an indicative evaluation of the acquired corpus and the first ASR models fine-tuned on this data, (iii) our social campaigns to boost and maintain this initiative.</abstract>
<identifier type="citekey">dargis-etal-2024-balsutalka</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.187</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>2080</start>
<end>2085</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages
%A Dargis, Roberts
%A Znotins, Arturs
%A Auzina, Ilze
%A Saulite, Baiba
%A Reinsone, Sanita
%A Dejus, Raivis
%A Klavinska, Antra
%A Gruzitis, Normunds
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F dargis-etal-2024-balsutalka
%X Open speech corpora of substantial size are seldom available for less-spoken languages, and this was recently the case also for Latvian with its 1.5M native speakers. While there exist several closed Latvian speech corpora of 100+ hours, used to train competitive models for automatic speech recognition (ASR), there were only a few tiny open datasets available at the beginning of 2023, the 18-hour Latvian Common Voice 13.0 dataset being the largest one. In the result of a successful national crowdsourcing initiative, organised jointly by several institutions, the size and speaker diversity of the Latvian Common Voice 17.0 release have increased more than tenfold in less than a year. A successful follow-up initiative was also launched for Latgalian, which has been recognized as an endangered historic variant of Latvian with 150k speakers. The goal of these initiatives is not only to enlarge the datasets but also to make them more diverse in terms of speakers and accents, text genres and styles, intonations, grammar and lexicon. They have already become considerable language resources for both improving ASR and conducting linguistic research. Since we use the Mozilla Common Voice platform to record and validate speech samples, this paper focuses on (i) the selection of text snippets to enrich the language data and to stimulate various intonations, (ii) an indicative evaluation of the acquired corpus and the first ASR models fine-tuned on this data, (iii) our social campaigns to boost and maintain this initiative.
%U https://aclanthology.org/2024.lrec-main.187
%P 2080-2085
Markdown (Informal)
[BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages](https://aclanthology.org/2024.lrec-main.187) (Dargis et al., LREC-COLING 2024)
ACL
- Roberts Dargis, Arturs Znotins, Ilze Auzina, Baiba Saulite, Sanita Reinsone, Raivis Dejus, Antra Klavinska, and Normunds Gruzitis. 2024. BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2080–2085, Torino, Italia. ELRA and ICCL.