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Models and activations for "Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation" ...
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Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation ...
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Models and activations for "Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation" ...
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Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation ...
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A thorough evaluation of the Language Environment Analysis (LENA) system
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In: Behav Res Methods (2021)
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A thorough evaluation of the Language Environment Analysis (LENA) system
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In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-03095997 ; Behavior Research Methods, Psychonomic Society, Inc, 2020, 53 (2), pp.467-486. ⟨10.3758/s13428-020-01393-5⟩ (2020)
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Analysis of Predictive Coding Models for Phonemic Representation Learning in Small Datasets ...
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ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings
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In: Behav Res Methods (2020)
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A computational model of early language acquisition from audiovisual experiences of young infants ...
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SylNet: An Adaptable End-to-End Syllable Count Estimator for Speech ...
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Is infant-directed speech interesting because it is surprising? – Linking properties of IDS to statistical learning and attention at the prosodic level ...
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Pre-linguistic segmentation of speech into syllable-like units ...
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Making predictable unpredictable with style – Behavioral and electrophysiological evidence for the critical role of prosodic expectations in the perception of prominence in speech ...
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Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks
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Abstract:
Infants’ speech perception adapts to the phonemic categories of their native language, a process assumed to be driven by the distributional properties of speech. This study investigates whether deep neural networks (DNNs), the current state-of-the-art in distributional feature learning, are capable of learning phoneme-like representations of speech in an unsupervised manner. We trained DNNs with unlabeled and labeled speech and analyzed the activations of each layer with respect to the phones in the input segments. The analyses reveal that the emergence of phonemic invariance in DNNs is dependent on the availability of phonemic labeling of the input during the training. No increased phonemic selectivity of the hidden layers was observed in the purely unsupervised networks despite successful learning of low-dimensional representations for speech. This suggests that additional learning constraints or more sophisticated models are needed to account for the emergence of phone-like categories in distributional learning operating on natural speech.
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Keyword:
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5805375/
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