Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech (L18-1)
Copied to clipboard
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. |
Similar Papers
Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems (2020.lrec-1)
Copied to clipboard
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. |
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages (2023.eacl-main)
Copied to clipboard
Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Rahmad Mahendra, Fajri Koto, Ade Romadhony, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Pascale Fung, Timothy Baldwin, Jey Han Lau, Rico Sennrich, Sebastian Ruder
| Challenge: | In Indonesia, many languages are endangered and some are even extinct due to the unavailability of data resources and benchmarks. |
| Approach: | They propose a high-quality multilingual parallel corpus that covers 10 local languages from Indonesia. |
| Outcome: | The proposed resource includes sentiment and machine translation datasets, and bilingual lexicons. |
Common Voice: A Massively-Multilingual Speech Corpus (2020.lrec-1)
Copied to clipboard
Rosana Ardila, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer, Michael Henretty, Reuben Morais, Lindsay Saunders, Francis Tyers, Gregor Weber
| Challenge: | Common Voice is a massively-multilingual collection of transcribed speech intended for speech technology research and development. |
| Approach: | They propose to use Mozilla’s DeepSpeech Speech-to-Text toolkit to perform multilingual automatic speech recognition experiments. |
| Outcome: | The proposed corpus is the largest in the public domain for speech recognition, both in terms of hours and languages. |
A New Massive Multilingual Dataset for High-Performance Language Technologies (2024.lrec-main)
Copied to clipboard
Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
| Challenge: | a new massive multilingual dataset is available for language modeling and machine translation training. |
| Approach: | They present a massive multilingual dataset using web crawls from the Internet Archive and CommonCrawl . they use open-source software tools and high-performance computing to acquire, manage and process large corpora . |
| Outcome: | The HPLT language resources is a massive multilingual dataset . it includes monolingual and bilingual corpora extracted from CommonCrawl and the Internet Archive . the results are published online at the journal journal cense4 . |
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
| Outcome: | The proposed corpus is searchable through a couple of well-established corpus infrastructures. |
Praaline: An Open-Source System for Managing, Annotating, Visualising and Analysing Speech Corpora (P18-4)
Copied to clipboard
| Challenge: | Praaline is an open-source software system for constituting and managing spoken language and multimodal corpora. |
| Approach: | They present the latest developments of Praaline, an open-source software system for constituting and managing spoken language and multimodal corpora. |
| Outcome: | The proposed system can be used for creating, managing, visualising and analysing spoken language and multimodal corpora. |
What Do Indonesians Really Need from Language Technology? A Nationwide Survey (2025.emnlp-main)
Copied to clipboard
| Challenge: | Despite efforts to develop NLP for Indonesia’s 700+ local languages, progress remains costly due to the need for direct engagement with native speakers. |
| Approach: | They conduct a nationwide survey to assess the actual needs of native Indonesian speakers. |
| Outcome: | The findings indicate that addressing language barriers is the most critical priority . concerns around privacy, bias, and the use of public data highlight the need for greater transparency and clear communication to support broader AI adoption. |
PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing datasets for Indian languages are limited in terms of coverage and size. |
| Approach: | They propose a multilingual and massively parallel summarization corpus focused on languages in India that provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs. |
| Outcome: | The proposed dataset provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs. |
BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages (2024.lrec-main)
Copied to clipboard
Roberts Dargis, Arturs Znotins, Ilze Auzina, Baiba Saulite, Sanita Reinsone, Raivis Dejus, Antra Klavinska, Normunds Gruzitis
| Challenge: | Latvian is a low-resource language for many NLP tasks, but most speech corpora are closed data . a crowdsourcing campaign to create a relatively large, diverse and open speech corpus for Latvian has been launched . |
| Approach: | a crowdsourcing campaign is helping to create an open speech corpus for Latvian . the goal is to enlarge the datasets and make them more diverse . authors use the opensource Mozilla Common Voice platform to validate speech samples . |
| Outcome: | a crowdsourcing initiative has increased the size and speaker diversity of the Latvian Common Voice 17.0 dataset by more than tenfold in less than a year. |
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)
Copied to clipboard
Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee
| Challenge: | a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data. |
| Approach: | They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language . |
| Outcome: | The proposed system can be used to train models in languages without standard writing systems. |