Computational modeling of phonetic and lexical learning in early language acquisition: Existing models and future directions

By Okko Johannes Räsänen. 2012

60 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Experimental and Cognitive Psychology, Cognitive Science & Psychology, Linguistics

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Citations in the corpus

Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks (2019, background): “From a scientific perspective, discrete representations could be useful in cognitive models to study phonetic category learning in human infants [15–18].”
A segmental framework for fully-unsupervised large-vocabulary speech recognition (2016, background): “Similar techniques are required to model how human infants acquire language from speech input [4], and for developing robotic applications that can learn a new language in an unknown environment [5,6].”
Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner (2016, positive): “put to discover candidate words (Ten Bosch & Cranen, 2007, see also Park & Glass, 2008; Muscariello, Gravier, & Bimbot, 2009, etc.), or to learn word-meaning associations (see a review in Räsänen, 2012, and a comprehensive model in Räsänen & Rasilo, 2015), although the speech was col”
An embedded segmental K-means model for unsupervised segmentation and clustering of speech (2017, background): “In addition, such methods may shed light on how human infants acquire language [2, 3].”
Fully unsupervised small-vocabulary speech recognition using a segmental Bayesian model (2015, methodology): “Meanwhile in the scientific cognitive modelling community, unsupervised techniques are used to model how infants learn phonetic categories and a lexicon for their native language [11].”
Visually Grounded Learning of Keyword Prediction from Untranscribed Speech (2017, background): “Speciﬁcally, we are interested in the setting considered in [15,16], where natural images of scenes are paired with spoken descriptions, and neither the images nor speech are labelled.”
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge (2020, methodology): “CER MOS Similarity ABX (%) Model (%) [1, 5] [1, 5] code aux Bitrate”