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Found 5 papers, 4 papers with code
Modelling word learning and recognition using visually grounded speech
We furthermore investigate whether vector quantisation, a technique for discrete representation learning, aids the model in the discovery and recognition of words.
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge
In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning.
Semantic sentence similarity: size does not always matter
This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge.
Language learning using Speech to Image retrieval
Humans learn language by interaction with their environment and listening to other humans.
The Nijmegen Corpus of Casual Czech
All speakers were native speakers of Czech, raised in Prague or in the region of Central Bohemia, and were between 19 and 26 years old.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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