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When the Easy Becomes Difficult: Factors Affecting the Acquisition of the English /iː/-/ɪ/ Contrast
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In: Frontiers in Communication ; 6 (2022)
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| 2 |
An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
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In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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Abstract:
International audience ; Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [SCCO.LING]Cognitive science/Linguistics; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; acoustic phonetics; code-switching; corpora development; discriminative model; Indian language identification; Language resources; language similarity; Machine learning; Signal processing systems Low-resourced languages
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URL: https://hal.inria.fr/hal-03616853/file/TALLIP_Overview.pdf
https://doi.org/10.1145/3523179
https://hal.inria.fr/hal-03616853
https://hal.inria.fr/hal-03616853/document
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| 3 |
Calculating semantic relatedness of lists of nouns using WordNet path length ...
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| 4 |
Impact of Sentence Representation Matching in Neural Machine Translation
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1313 (2022)
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| 5 |
The Research on Consistency Checking and Improvement of Probabilistic Linguistic Preference Relation Based on Similarity Measure and Minimum Adjustment Model
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In: Mathematics; Volume 10; Issue 9; Pages: 1369 (2022)
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| 6 |
Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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| 10 |
Capability Language Processing (CLP): Classification and Ranking of Manufacturing Suppliers Based on Unstructured Capability Data
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| 11 |
Usage-Based Contact Linguistics : Effects of Frequency and Similarity in Language Contact
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| 12 |
TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing
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| 13 |
“Language thinking” from the perspective of systemic linguistics
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In: Russian Journal of Linguistics, Vol 26, Iss 1, Pp 224-244 (2022) (2022)
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| 14 |
Automatic Detection of Plagiarism in Writing
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In: Studies in Applied Linguistics & TESOL, Vol 21, Iss 2 (2022) (2022)
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| 15 |
Automatic Detection of Plagiarism in Writing
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In: Studies in Applied Linguistics & TESOL, Vol 21, Iss 2 (2022) (2022)
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| 16 |
Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms
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In: NL4IA Workshop Proceedings ; https://hal.archives-ouvertes.fr/hal-03474696 ; NL4IA Workshop Proceedings, Nov 2021, Milan, Italy (2021)
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| 17 |
Investigating the impact of preprocessing on document embedding: an empirical comparison
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In: ISSN: 1759-1163 ; EISSN: 1759-1171 ; International Journal of Data Mining, Modelling and Management ; https://hal.inrae.fr/hal-03574696 ; International Journal of Data Mining, Modelling and Management, Inderscience, 2021, 13 (4), pp.351-363 (2021)
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| 18 |
Language distance in orthographic transparency affects cross-language pattern similarity between native and non-native languages.
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In: Human brain mapping, vol 42, iss 4 (2021)
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| 19 |
Avoiding gender ambiguous pronouns in French
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.archives-ouvertes.fr/hal-03374279 ; Cognition, Elsevier, 2021 (2021)
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