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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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Learning from the acoustic signal: Error-driven learning of low-level acoustics discriminates vowel and consonant pairs ...
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Learning from the acoustic signal: Error-driven learning of low-level acoustics discriminates vowel and consonant pairs ...
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Abstract:
Until the last couple of decades, research on speech acquisition generally assumed that infants were born with innate knowledge of a universal set of phonetic features that occurred across all the world's languages and that learning one's native language involved selecting the appropriate subset from this broader group. Over the last two decades, this account has given way to the idea that speech sounds are learned via general learning mechanisms. Statistical clustering models have become the most common way to explain how infants learn the sounds of their language. Over the first few months of life, infants go from being able to discriminate the sounds of all languages, to perceiving speech sounds in a way that is increasingly honed to their native language. However, recent empirical and computational evidence suggests that purely statistical clustering methods may not be sufficient to explain speech sound acquisition. The present study used discriminative, error-driven learning, an implementation of the ...
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Keyword:
error-driven learning; discriminative learning; statistical learning; Rescorla-Wagner model; speech acquisition; first language acquisition
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URL: https://dx.doi.org/10.5281/zenodo.3992583 https://zenodo.org/record/3992583
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Discriminative ridge regression algorithm for adaptation in statistical machine translation
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A comparison of discriminative training criteria for continuous space translation models
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In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-01621763 ; Machine Translation, Springer Verlag, 2017, 1-2, 31, pp.19-33 (2017)
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Log-Linear Weight Optimization Using Discriminative Ridge Regression Method in Statistical Machine Translation
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Multi-Concept Learning With Large-Scale Multimedia Lexicons
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In: Proceedings of 2nd International Conference on Information Processing (ICIP 2008) (2015)
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A Discriminative Training Procedure for Continuous Translation Models
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In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, ; Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01635000 ; Conference on Empirical Methods in Natural Language Processing, The Association for Computational Linguistics, Sep 2015, Lisbon, Portugal. pp.1046 - 1052 ; http://www.emnlp2015.org/ (2015)
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Discriminative training of a phoneme confusion model for a dynamic lexicon in ASR
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In: Interspeech 2013 ; Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01843427 ; Annual Conference of the International Speech Communication Association, Jan 2013, Lyon, France (2013)
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Vision Based Hand Gesture Recognition Using Generative And Discriminative Stochastic Models ...
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Vision Based Hand Gesture Recognition Using Generative And Discriminative Stochastic Models ...
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Discriminative Alignment Models For Statistical Machine Translation ; Modèles Discriminants d'Alignement Pour La Traduction Automatique Statistique
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In: https://tel.archives-ouvertes.fr/tel-00720250 ; Other [cs.OH]. Université Paris Sud - Paris XI, 2012. English. ⟨NNT : 2012PA112104⟩ (2012)
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Modeling variable dependencies between characters in Chinese information retrieval
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In: http://www.iro.umontreal.ca/~nie/Publication/shi-airs10.pdf (2010)
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Linear discriminant model for information retrieval
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In: http://research.microsoft.com/~jfgao/paper/sigir05.p-121.gao.pdf (2005)
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Linear discriminant model for information retrieval
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In: http://www.iro.umontreal.ca/~nie/Publication/gao-sigir05.pdf (2005)
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discriminant model for information retrieval
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In: http://cran.r-project.org/web/packages/quantreg/quantreg.pdf (2005)
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Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
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In: http://jmlr.org/papers/volume11/pernkopf10a/pernkopf10a.pdf
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Which is More Suitable for Chinese Word Segmentation, the Generative Model or the Discriminative One? F∗
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In: http://www.nlpr.ia.ac.cn/cip/zongpublications/2009/2009 paclic23-827-834-wang kun.pdf
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Which is More Suitable for Chinese Word Segmentation, the Generative Model or the Discriminative One? F∗
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In: http://www.aclweb.org/anthology-new/Y/Y09/Y09-2047.pdf
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