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GlotLID: Language Identification for Low-Resource Languages

Findings of the Association for Computational Linguistics: EMNLP 2023, 2023
Several recent papers have published good solutions for language identification (LID) for about 300 high-resource and medium-resource languages. However, there is no LID available that (i) covers a wide range of low-resource languages, (ii) is rigorously
Amir Hossein Kargaran, Ayyoob Imani, François Yvon, Hinrich Schütze   +3 more
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Improving Automatic Speech Recognition Performance for Low-Resource Languages With Self-Supervised Models

IEEE Journal on Selected Topics in Signal Processing, 2022
Speech self-supervised learning has attracted much attention due to its promising performance in multiple downstream tasks, and has become a new growth engine for speech recognition in low-resource languages.
Jing Zhao, Wei-Qiang Zhang
exaly   +2 more sources

Speech Separation for Low-Resource Languages

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters.
Marvin Borsdorf, Zexu Pan, Pascal Himmelmann, Haizhou Li, Tanja Schultz   +4 more
semanticscholar   +2 more sources

Transformers for Low-Resource Languages: Is Féidir Linn!

CoRR, 2021
The Transformer model is the state-of-the-art in Machine Translation. However and in general and neural translation models often under perform on language pairs with insufficient training data.
Séamus Lankford, H. Alfi, Andy Way
semanticscholar   +5 more sources

On Limitations of LLM as Annotator for Low Resource Languages

CoRR
Low-resource languages face significant challenges due to the lack of sufficient linguistic data, resources, and tools for tasks such as supervised learning, annotation, and classification.
Suramya Jadhav, Abhay Gajanan Shanbhag, Amogh Thakurdesai, Ridhima Sinare, Raviraj Joshi   +4 more
semanticscholar   +3 more sources

A Study on Low-resource Language Identification

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019
Modern language identification (LID) systems require a large amount of data to train language-discriminative models, either statistical (e.g., i-vector) or neural (e.g., x-vector). Unfortunately, most of languages in the world have very limited accumulation of data resources, which result in limited performance on most languages.
Zhaodi Qi, Yong Ma, Mingliang Gu
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Speech and Language Technologies for Low-Resource Languages

Communications in Computer and Information Science, 2023
exaly   +2 more sources

Phone Distribution Estimation for Low Resource Languages

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
Phones are critical components in various computational linguistic fields, for example, phone distributions could be helpful in speech recognition and speech synthesis. Traditional approaches to estimate phone distributions typically involve G2P systems which are either manually designed by linguists or trained on large datasets.
Xinjian Li, Juncheng Li 0001, Jiali Yao, Alan W. Black, Florian Metze   +4 more
openaire   +1 more source

Natural language processing applications for low-resource languages

Natural Language Processing
Natural language processing (NLP) has significantly advanced our ability to model and interact with human language through technology. However, these advancements have disproportionately benefited high-resource languages with abundant data for training ...
Partha Pakray, Alexander Gelbukh, Sivaji Bandyopadhyay   +2 more
semanticscholar   +1 more source

Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque

Conference on Empirical Methods in Natural Language Processing
Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to conventional instruction adaptation pipelines in low-resource scenarios ...
Oscar Sainz, Naiara Pérez, Julen Etxaniz, Joseba Fernandez de Landa, Itziar Aldabe, Iker Garc'ia-Ferrero, Aimar Zabala, Ekhi Azurmendi, Germán Rigau, Eneko Agirre, Mikel Artetxe, A. Soroa   +11 more
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