Search Results for author: Rodolfo Zevallos

Found 7 papers, 1 papers with code

Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition

no code implementations LREC 2022 Rodolfo Zevallos, Luis Camacho, Nelsi Melgarejo

Huqariq includes four native languages of Peru, and it is expected that by the year 2022, it can reach up to 20 native languages out of the 48 native languages in Peru.

Automatic Speech Recognition Language Identification +1

WordNet-QU: Development of a Lexical Database for Quechua Varieties

no code implementations COLING 2022 Nelsi Melgarejo, Rodolfo Zevallos, Hector Gomez, John E. Ortega

In the effort to minimize the risk of extinction of a language, linguistic resources are fundamental.

Data Augmentation for Low-Resource Quechua ASR Improvement

no code implementations14 Jul 2022 Rodolfo Zevallos, Nuria Bel, Guillermo Cámbara, Mireia Farrús, Jordi Luque

In this paper we describe our data augmentation approach to improve the results of ASR models for low-resource and agglutinative languages.

Automatic Speech Recognition speech-recognition +1

Huqariq: A Multilingual Speech Corpus of Native Languages of Peru for Speech Recognition

no code implementations12 Jul 2022 Rodolfo Zevallos, Luis Camacho, Nelsi Melgarejo

Huqariq includes four native languages of Peru, and it is expected that by the end of the year 2022, it can reach up to 20 native languages out of the 48 native languages in Peru.

Automatic Speech Recognition Language Identification +1

Preparing an Endangered Language for the Digital Age: The Case of Judeo-Spanish

2 code implementations EURALI (LREC) 2022 Alp Öktem, Rodolfo Zevallos, Yasmin Moslem, Güneş Öztürk, Karen Şarhon

We develop machine translation and speech synthesis systems to complement the efforts of revitalizing Judeo-Spanish, the exiled language of Sephardic Jews, which survived for centuries, but now faces the threat of extinction in the digital age.

Machine Translation Speech Synthesis +2

Text-To-Speech Data Augmentation for Low Resource Speech Recognition

no code implementations1 Apr 2022 Rodolfo Zevallos

In this research, an 8. 73% improvement in the word-error-rate (WER) of the ASR model is obtained using a combination of synthetic text and synthetic speech.

Automatic Speech Recognition Data Augmentation +1

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