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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 20 of 20 1 Models and activations for "Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation" ... Khorrami, Khazar; Räsänen, Okko. - : Zenodo, 2021 BASE Show details 2 Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation ... Khorrami, Khazar; Räsänen, Okko. - : arXiv, 2021 BASE Show details 3 Models and activations for "Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation" ... Khorrami, Khazar; Räsänen, Okko. - : Zenodo, 2021 BASE Show details 4 Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation ... Khorrami, Khazar; Räsänen, Okko. - : Carnegie Mellon University Library Publishing Service, 2021 BASE Show details 5 A thorough evaluation of the Language Environment Analysis (LENA) system Cristia, Alejandrina; Lavechin, Marvin; Scaff, Camila... In: Behav Res Methods (2021) BASE Show details 6 ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings [<Journal>] Räsänen, Okko [Verfasser]; Seshadri, Shreyas [Verfasser]; Lavechin, Marvin [Verfasser]. DNB Subject Category Language Show details 7 A thorough evaluation of the Language Environment Analysis (LENA) system Cristia, Alejandrina; Lavechin, Marvin; Scaff, Camila... 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. &#x27E8;10.3758/s13428-020-01393-5&#x27E9; (2020) BASE Show details 8 Analysis of Predictive Coding Models for Phonemic Representation Learning in Small Datasets ... Blandón, María Andrea Cruz; Räsänen, Okko. - : arXiv, 2020 BASE Show details 9 ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings Räsänen, Okko; Seshadri, Shreyas; Lavechin, Marvin... In: Behav Res Methods (2020) BASE Show details 10 A computational model of early language acquisition from audiovisual experiences of young infants ... Räsänen, Okko; Khorrami, Khazar. - : arXiv, 2019 BASE Show details 11 SylNet: An Adaptable End-to-End Syllable Count Estimator for Speech ... Seshadri, Shreyas; Räsänen, Okko. - : arXiv, 2019 BASE Show details 12 Is infant-directed speech interesting because it is surprising? – Linking properties of IDS to statistical learning and attention at the prosodic level ... Räsänen, Okko; Kakouros, Sofoklis; Soderstrom, Melanie. - : PsyArXiv, 2017 BASE Show details 13 Pre-linguistic segmentation of speech into syllable-like units ... Räsänen, Okko; Doyle, Gabriel; Frank, Michael. - : PsyArXiv, 2017 BASE Show details 14 Zs2017 Aaltolag Submission #1 (Mostly Testing) ... Räsänen, Okko; Seshadri, Shreyas. - : Zenodo, 2017 BASE Show details 15 Zs2017 Aaltolag Submission #1 (Mostly Testing) ... Räsänen, Okko; Seshadri, Shreyas. - : Zenodo, 2017 BASE Show details 16 Making predictable unpredictable with style – Behavioral and electrophysiological evidence for the critical role of prosodic expectations in the perception of prominence in speech ... Kakouros, Sofoklis; Salminen, Nelli; Räsänen, Okko. - : PsyArXiv, 2017 BASE Show details 17 Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks Räsänen, Okko; Nagamine, Tasha; Mesgarani, Nima. - 2016 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. Keyword: Article URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5805375/ BASE Hide details 18 Feedback and imitation by a caregiver guides a virtual infant to learn native phonemes and the skill of speech inversion Rasilo, Heikki; Räsänen, Okko; Laine, Unto K. In: Speech communication. - Amsterdam [u.a.] : Elsevier 55 (2013) 9, 909-931 OLC Linguistik Show details 19 Computational modeling of phonetic and lexical learning in early language acquisition: existing models and future directions Räsänen, Okko In: Speech communication. - Amsterdam [u.a.] : Elsevier 54 (2012) 9, 975-997 BLLDB OLC Linguistik Show details 20 A computational model of word segmentation from continuous speech using transitional probabilities of atomic acoustic events Räsänen, Okko In: Cognition. - Amsterdam [u.a] : Elsevier 120 (2011) 2, 149-176 BLLDB OLC Linguistik Show details

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