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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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Computational Models in Electroencephalography.
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In: Brain topography, vol 35, iss 1 (2022)
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It’s your turn: The dynamics of conversational turn-taking in father-child and mother-child interaction ...
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Becoming the Metalinguistic Mind: The Development of Metalinguistic Abilities in Children from 5 to 7
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In: Children; Volume 9; Issue 4; Pages: 550 (2022)
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Emotion, discourse, and linguistic diversity: Emotions in grammar and discourse in northern Australia ...
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Robot skills and cognitive performance of preschool children
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The origins of trust: Humans’ reliance on communicative cues supersedes firsthand experience during the second year of life
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In: ISSN: 1363-755X ; EISSN: 1467-7687 ; Developmental Science ; https://hal.archives-ouvertes.fr/hal-03624277 ; Developmental Science, Wiley, 2021, ⟨10.1111/desc.13223⟩ (2021)
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Medical teachers’ opinions about students with neurodevelopmental disorders and their management
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In: ISSN: 1472-6920 ; BMC Medical Education ; https://hal-univ-fcomte.archives-ouvertes.fr/hal-03630125 ; BMC Medical Education, BioMed Central, 2021, 21 (1), pp.16. ⟨10.1186/s12909-020-02413-w⟩ (2021)
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The Acquisition of Verbal Epistemic Stance Marking during Study Abroad: The Case of je pense in L2 French
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In: EISSN: 2226-471X ; Languages ; https://hal.archives-ouvertes.fr/hal-03476036 ; Languages, MDPI, 2021, 6 (4), pp.196. ⟨10.3390/languages6040196⟩ (2021)
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Sobre o impacto da constituência silábica na repetição de pseudopalavras: dados preliminares do desenvolvimento típico para a validação do LITMUS-QU-NWR-EP
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In: ISSN: 2183-9077 ; Revista da Associação Portuguesa de Linguística ; https://hal.archives-ouvertes.fr/hal-03394736 ; Revista da Associação Portuguesa de Linguística, 2021, pp.87-104. ⟨10.26334/2183-9077/rapln8ano2021a7⟩ (2021)
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Familiar words can serve as a semantic seed for syntactic bootstrapping
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In: ISSN: 1363-755X ; EISSN: 1467-7687 ; Developmental Science ; https://hal.archives-ouvertes.fr/hal-03098829 ; Developmental Science, Wiley, 2021, 24 (1), pp.e13010. ⟨10.1111/desc.13010⟩ (2021)
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Toddlers exploit referential and syntactic cues to flexibly adapt their interpretation of novel verb meanings
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In: ISSN: 0022-0965 ; EISSN: 1096-0457 ; Journal of Experimental Child Psychology ; https://hal.archives-ouvertes.fr/hal-03468213 ; Journal of Experimental Child Psychology, Elsevier, 2021, 203, pp.105017. ⟨10.1016/j.jecp.2020.105017⟩ (2021)
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Hierarchical Temporal Structure and Convergence in Development, Performance, and Bilingualism
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Stem similarity modulates infants' acquisition of phonological alternations.
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Arbitrary but predictive cues support attention to overlooked features
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Mobile Media and Young Children's Cognitive Skills: A Review.
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In: Academic pediatrics, vol 21, iss 6 (2021)
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Parent Language Input Prior to School Forecasts Change in Children's Language-Related Cortical Structures During Mid-Adolescence.
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