Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969686" target="_blank" >RIV/49777513:23520/23:43969686 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-speech.org/archive/interspeech_2023/lehecka23_interspeech.html" target="_blank" >https://www.isca-speech.org/archive/interspeech_2023/lehecka23_interspeech.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2023-872" target="_blank" >10.21437/Interspeech.2023-872</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech

Popis výsledku v původním jazyce

This paper is a step forward in our effort to make vast oral history archives more accessible to the public and researchers by breaking down the decoding barriers between the knowledge encoded in the spoken testimonies and users who want to search for the information of their interest. We present new Transformer-based monolingual models suitable for speech recognition of oral history archives in English, German, and Czech. Our experiments show that although the all-purpose speech recognition systems have recently made tremendous progress, the transcription of oral history archives is still a challenging task for them; our tailored models significantly outperformed larger public multilingual models and scored new state-of-the-art results on all tested datasets. Due to the 2-phase fine-tuning process, our models are robust and can be used for oral history archives of various domains. We publicly release our models within a public speech recognition service.

Název v anglickém jazyce

Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech

Popis výsledku anglicky

This paper is a step forward in our effort to make vast oral history archives more accessible to the public and researchers by breaking down the decoding barriers between the knowledge encoded in the spoken testimonies and users who want to search for the information of their interest. We present new Transformer-based monolingual models suitable for speech recognition of oral history archives in English, German, and Czech. Our experiments show that although the all-purpose speech recognition systems have recently made tremendous progress, the transcription of oral history archives is still a challenging task for them; our tailored models significantly outperformed larger public multilingual models and scored new state-of-the-art results on all tested datasets. Due to the 2-phase fine-tuning process, our models are robust and can be used for oral history archives of various domains. We publicly release our models within a public speech recognition service.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA22-27800S" target="_blank" >GA22-27800S: Využití vícemodálních Transformerů pro přirozenější hlasový dialog</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

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

201-205

Název nakladatele

International Speech Communication Association

Místo vydání

New York

Místo konání akce

Dublin, Ireland

Datum konání akce

20. 8. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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