Search Results for author:
Found 10 papers, 3 papers with code
Visually Grounded Speech Models have a Mutual Exclusivity Bias
To simulate prior acoustic and visual knowledge, we experiment with several initialisation strategies using pretrained speech and vision networks.
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation
We consider how a recent contrastive learning loss can be used in both the purely unsupervised and multilingual transfer settings.
A phonetic model of non-native spoken word processing
We then test the model on a spoken word processing task, showing that phonology may not be necessary to explain some of the word processing effects observed in non-native speakers.
Evaluating computational models of infant phonetic learning across languages
In the first year of life, infants' speech perception becomes attuned to the sounds of their native language.
Improved acoustic word embeddings for zero-resource languages using multilingual transfer
We consider three multilingual recurrent neural network (RNN) models: a classifier trained on the joint vocabularies of all training languages; a Siamese RNN trained to discriminate between same and different words from multiple languages; and a correspondence autoencoder (CAE) RNN trained to reconstruct word pairs.
Analyzing autoencoder-based acoustic word embeddings
To better understand the applications of AWEs in various downstream tasks and in cognitive modeling, we need to analyze the representation spaces of AWEs.
Multilingual acoustic word embedding models for processing zero-resource languages
Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments.
Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection
The cognitive mechanisms needed to account for the English past tense have long been a subject of debate in linguistics and cognitive science.
