2024
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Bi-dialectal ASR of Armenian from Naturalistic and Read Speech
Malajyan Arthur
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Victoria Khurshudyan
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Karen Avetisyan
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Hossep Dolatian
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Damien Nouvel
Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024
The paper explores the development of Automatic Speech Recognition (ASR) models for Armenian, by using data from two standard dialects (Eastern Armenian and Western Armenian). The goal is to develop a joint bi-variational model. We achieve state-of-the-art results. Results from our ASR experiments demonstrate the impact of dataset selection and data volume on model performance. The study reveals limited transferability between dialects, although integrating datasets from both dialects enhances overall performance. The paper underscores the importance of dataset diversity and volume in ASR model training for under-resourced languages like Armenian.
2022
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Proceedings of the Workshop on Processing Language Variation: Digital Armenian (DigitAm) within the 13th Language Resources and Evaluation Conference
Victoria Khurshudyan
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Nadi Tomeh
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Damien Nouvel
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Anaid Donabedian
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Chahan Vidal-Gorene
Proceedings of the Workshop on Processing Language Variation: Digital Armenian (DigitAm) within the 13th Language Resources and Evaluation Conference
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Eastern Armenian National Corpus: State of the Art and Perspectives
Victoria Khurshudyan
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Timofey Arkhangelskiy
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Misha Daniel
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Vladimir Plungian
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Dmitri Levonian
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Alex Polyakov
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Sergei Rubakov
Proceedings of the Workshop on Processing Language Variation: Digital Armenian (DigitAm) within the 13th Language Resources and Evaluation Conference
Eastern Armenian National Corpus (EANC) is a comprehensive corpus of Modern Eastern Armenian with about 110 million tokens, covering written and oral discourses from the mid-19th century to the present. The corpus is provided with morphological, semantic and metatext annotation, as well as English translations. EANC is open access and available at www.eanc.net.
2020
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Recycling and Comparing Morphological Annotation Models for Armenian Diachronic-Variational Corpus Processing
Chahan Vidal-Gorène
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Victoria Khurshudyan
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Anaïd Donabédian-Demopoulos
Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects
Armenian is a language with significant variation and unevenly distributed NLP resources for different varieties. An attempt is made to process an RNN model for morphological annotation on the basis of different Armenian data (provided or not with morphologically annotated corpora), and to compare the annotation results of RNN and rule-based models. Different tests were carried out to evaluate the reuse of an unspecialized model of lemmatization and POS-tagging for under-resourced language varieties. The research focused on three dialects and further extended to Western Armenian with a mean accuracy of 94,00 % in lemmatization and 97,02% in POS-tagging, as well as a possible reusability of models to cover different other Armenian varieties. Interestingly, the comparison of an RNN model trained on Eastern Armenian with the Eastern Armenian National Corpus rule-based model applied to Western Armenian showed an enhancement of 19% in parsing. This model covers 88,79% of a short heterogeneous dataset in Western Armenian, and could be a baseline for a massive corpus annotation in that standard. It is argued that an RNN-based model can be a valid alternative to a rule-based one giving consideration to such factors as time-consumption, reusability for different varieties of a target language and significant qualitative results in morphological annotation.