Alena Butryna


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Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech
Adriana Guevara-Rukoz | Isin Demirsahin | Fei He | Shan-Hui Cathy Chu | Supheakmungkol Sarin | Knot Pipatsrisawat | Alexander Gutkin | Alena Butryna | Oddur Kjartansson
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper we present a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources, focusing on various South American dialects of Spanish. We first present public speech datasets for Argentinian, Chilean, Colombian, Peruvian, Puerto Rican and Venezuelan Spanish specifically constructed with text-to-speech applications in mind using crowd-sourcing. We then compare the monodialectal voices built with minimal data to a multidialectal model built by pooling all the resources from all dialects. Our results show that the multidialectal model outperforms the monodialectal baseline models. We also experiment with a “zero-resource” dialect scenario where we build a multidialectal voice for a dialect while holding out target dialect recordings from the training data.

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Open-Source High Quality Speech Datasets for Basque, Catalan and Galician
Oddur Kjartansson | Alexander Gutkin | Alena Butryna | Isin Demirsahin | Clara Rivera
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

This paper introduces new open speech datasets for three of the languages of Spain: Basque, Catalan and Galician. Catalan is furthermore the official language of the Principality of Andorra. The datasets consist of high-quality multi-speaker recordings of the three languages along with the associated transcriptions. The resulting corpora include over 33 hours of crowd-sourced recordings of 132 male and female native speakers. The recording scripts also include material for elicitation of global and local place names, personal and business names. The datasets are released under a permissive license and are available for free download for commercial, academic and personal use. The high-quality annotated speech datasets described in this paper can be used to, among other things, build text-to-speech systems, serve as adaptation data in automatic speech recognition and provide useful phonetic and phonological insights in corpus linguistics.