Automatic Development of ASR System for an Under-Resourced Language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F18%3A00006127" target="_blank" >RIV/46747885:24220/18:00006127 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TSP.2018.8441243" target="_blank" >http://dx.doi.org/10.1109/TSP.2018.8441243</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2018.8441243" target="_blank" >10.1109/TSP.2018.8441243</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Development of ASR System for an Under-Resourced Language

Popis výsledku v původním jazyce

This paper deals with an automatic development of language specific parts (such as vocabulary, language model (LM) and acoustic model (AM)) for Automatic Speech Recognition system. It describes our automatic approach and whole process of adapting of ASR system for Latvian language. We have already developed an approach consisted of methods and tools for adapting our ASR system. The approach utilizes only freely available audio and text data which can be found on the Internet. Vocabulary and LM are automatically created from text downloaded from news websites. AM is created using cross-lingual bootstrapping and lightly supervised re-training process from Latvian TV and radio broadcast data and from archive of Latvian parliament sessions. We showed that it is possible to obtain more than 100 hours of automatically annotated speech in only three months and create system that can achieve performance with WER values in range from 15.3 to 27.8 % with minimum human effort.

Název v anglickém jazyce

Automatic Development of ASR System for an Under-Resourced Language

Popis výsledku anglicky

This paper deals with an automatic development of language specific parts (such as vocabulary, language model (LM) and acoustic model (AM)) for Automatic Speech Recognition system. It describes our automatic approach and whole process of adapting of ASR system for Latvian language. We have already developed an approach consisted of methods and tools for adapting our ASR system. The approach utilizes only freely available audio and text data which can be found on the Internet. Vocabulary and LM are automatically created from text downloaded from news websites. AM is created using cross-lingual bootstrapping and lightly supervised re-training process from Latvian TV and radio broadcast data and from archive of Latvian parliament sessions. We showed that it is possible to obtain more than 100 hours of automatically annotated speech in only three months and create system that can achieve performance with WER values in range from 15.3 to 27.8 % with minimum human effort.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

Projekt

<a href="/cs/project/TH03010018" target="_blank" >TH03010018: DeepSpot - Multilingvální technologie pro detekci a včasné upozornění</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

41st International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-153864695-3

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

100-103

Název nakladatele

IEEE

Místo vydání

Řecko

Místo konání akce

Atény, Řecko

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000454845100023