Under-Represented Speech Dataset from Open Data: Case Study on the Romanian Language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AU9ASJ3FY" target="_blank" >RIV/00216208:11320/25:U9ASJ3FY - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206580176&doi=10.3390%2fapp14199043&partnerID=40&md5=476c2e940fb6ecdc26782e719321a107" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206580176&doi=10.3390%2fapp14199043&partnerID=40&md5=476c2e940fb6ecdc26782e719321a107</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app14199043" target="_blank" >10.3390/app14199043</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Under-Represented Speech Dataset from Open Data: Case Study on the Romanian Language

Popis výsledku v původním jazyce

This paper introduces the USPDATRO dataset. This is a speech dataset, in the Romanian language, constructed from open data, focusing on under-represented voice types (children, young and old people, and female voices). The paper covers the methodology behind the dataset construction, specific details regarding the dataset, and evaluation of existing Romanian Automatic Speech Recognition (ASR) systems, with different architectures. Results indicate that more under-represented speech content is needed in the training of ASR systems. Our approach can be extended to other low-resourced languages, as long as open data are available. © 2024 by the authors.

Název v anglickém jazyce

Under-Represented Speech Dataset from Open Data: Case Study on the Romanian Language

Popis výsledku anglicky

This paper introduces the USPDATRO dataset. This is a speech dataset, in the Romanian language, constructed from open data, focusing on under-represented voice types (children, young and old people, and female voices). The paper covers the methodology behind the dataset construction, specific details regarding the dataset, and evaluation of existing Romanian Automatic Speech Recognition (ASR) systems, with different architectures. Results indicate that more under-represented speech content is needed in the training of ASR systems. Our approach can be extended to other low-resourced languages, as long as open data are available. © 2024 by the authors.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Applied Sciences (Switzerland)

ISSN

2076-3417

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85206580176