Combining Multilingual Resources and Models to Develop State-of-the-Art E2E ASR for Swedish

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F23%3A00011571" target="_blank" >RIV/46747885:24220/23:00011571 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-archive.org/interspeech_2023/mateju23_interspeech.html" target="_blank" >https://www.isca-archive.org/interspeech_2023/mateju23_interspeech.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2023-737" target="_blank" >10.21437/Interspeech.2023-737</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining Multilingual Resources and Models to Develop State-of-the-Art E2E ASR for Swedish

Popis výsledku v původním jazyce

In terms of automatic speech recognition (ASR), Swedish belongs to the group of less-resourced languages, as publicly available training data is limited to a few hundred hours of mostly read speech. To acquire larger amounts of more realistic data, we investigate the existing multilingual approaches, and also propose two new ones, which combine Swedish with previously created Norwegian data and models. We use them for efficient automatic harvesting of spoken Swedish from broadcast, parliament, YouTube, and audiobook archives. The combined models significantly speed up the harvesting process and improve the final Swedish end-to-end (E2E) ASR system. We evaluate it on datasets covering various applications and domains; they provide performance better than the state-of-the-art commercial cloud services. We have made all of our test datasets publicly available for future comparative experiments.

Název v anglickém jazyce

Combining Multilingual Resources and Models to Develop State-of-the-Art E2E ASR for Swedish

Popis výsledku anglicky

In terms of automatic speech recognition (ASR), Swedish belongs to the group of less-resourced languages, as publicly available training data is limited to a few hundred hours of mostly read speech. To acquire larger amounts of more realistic data, we investigate the existing multilingual approaches, and also propose two new ones, which combine Swedish with previously created Norwegian data and models. We use them for efficient automatic harvesting of spoken Swedish from broadcast, parliament, YouTube, and audiobook archives. The combined models significantly speed up the harvesting process and improve the final Swedish end-to-end (E2E) ASR system. We evaluate it on datasets covering various applications and domains; they provide performance better than the state-of-the-art commercial cloud services. We have made all of our test datasets publicly available for future comparative experiments.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TO01000027" target="_blank" >TO01000027: NORDTRANS - Technologie pro automatický přepis řeči ve vybraných severských jazycích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

ISBN

—

ISSN

2308-457X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

3252 - 3256

Název nakladatele

ISCA

Místo vydání

Dublin

Místo konání akce

Dublin, Irsko

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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