Papers by Oddur Kjartansson
Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech (L18-1)
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Jaka Aris Eko Wibawa, Supheakmungkol Sarin, Chenfang Li, Knot Pipatsrisawat, Keshan Sodimana, Oddur Kjartansson, Alexander Gutkin, Martin Jansche, Linne Ha
| Challenge: | Using multi-speaker text-to-speech systems, we build systems for Javanese and Sundanese . progress in this direction is difficult because languages in the long tail of the distribution of the majority of the world's languages lack adequate linguistic resources . |
| Approach: | They present multi-speaker text-to-speech corpora for Javanese and Sundanese . they use mixed-gender recordings to build multi-language text-based systems . |
| Outcome: | The proposed multi-speaker text-to-speech systems outperform the systems constructed from a single language. |
Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech (2020.lrec-1)
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Adriana Guevara-Rukoz, Isin Demirsahin, Fei He, Shan-Hui Cathy Chu, Supheakmungkol Sarin, Knot Pipatsrisawat, Alexander Gutkin, Alena Butryna, Oddur Kjartansson
| Challenge: | Using crowd-sourced datasets, we build a text-to-speech voice for a new dialect in a language with existing resources. |
| Approach: | They propose a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources using crowd-sourcing. |
| Outcome: | The proposed model outperforms baseline models in a “zero-resource” dialect scenario while holding out target dialect recordings from the training data. |
Open-source Multi-speaker Corpora of the English Accents in the British Isles (2020.lrec-1)
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| Challenge: | Using a dataset of high-quality audio, the authors examine the accents of 120 volunteers in the British Isles. |
| Approach: | They present a dataset of high-quality audio of English sentences recorded by volunteers with different accents of the British Isles. |
| Outcome: | The transcribed audio includes pronunciations of global locations, major airlines and common personal names in different accents. |
Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech (2020.lrec-1)
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Yin May Oo, Theeraphol Wattanavekin, Chenfang Li, Pasindu De Silva, Supheakmungkol Sarin, Knot Pipatsrisawat, Martin Jansche, Oddur Kjartansson, Alexander Gutkin
| Challenge: | Using crowd-sourced speech corpus and finite-state transducer grammars, we build a text-to-speech system for Burmese, a tonal Southeast Asian language from the Sino-Tibetan family. |
| Approach: | They propose an open-source crowd-sourced multi-speaker speech corpus and finite-state grammars for performing grapheme-to-phoneme conversion for Burmese. |
| Outcome: | The proposed system performs well for Burmese in a low-resource setting. |
Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems (2020.lrec-1)
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Fei He, Shan-Hui Cathy Chu, Oddur Kjartansson, Clara Rivera, Anna Katanova, Alexander Gutkin, Isin Demirsahin, Cibu Johny, Martin Jansche, Supheakmungkol Sarin, Knot Pipatsrisawat
| Challenge: | We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . the datasets are primarily intended for use in text-to-speech applications, such as constructing multilingual voices or language adaptation. |
| Approach: | They present a free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . they use it to build a multilingual text-to-speech model that can be scaled to other languages of interest. |
| Outcome: | The proposed model produces good quality voices with MOS > 3.6 for all the languages tested. |