Papers by Supheakmungkol Sarin
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (2022.tacl-1)
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Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
| Challenge: | Lower-resource corpora have systematic issues, including mislabeled or nonstandard/ambiguous language codes. |
| Approach: | They manually audit the quality of 205 language-specific corpora released with five major public datasets. |
| Outcome: | The results show that lower-resource corpora have systematic issues even for non-proficient speakers. |
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. |
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. |