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Found 6 papers, 2 papers with code
Discrete representations in neural models of spoken language
The distributed and continuous representations used by neural networks are at odds with representations employed in linguistics, which are typically symbolic.
Learning to Understand Child-directed and Adult-directed Speech
Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation.
The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue
This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation.
On the difficulty of a distributional semantics of spoken language
In the domain of unsupervised learning most work on speech has focused on discovering low-level constructs such as phoneme inventories or word-like units.
Representations of language in a model of visually grounded speech signal
We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space.
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning
We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes.
