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Found 6 papers, 5 papers with code
Towards visually prompted keyword localisation for zero-resource spoken languages
We formalise this task and call it visually prompted keyword localisation (VPKL): given an image of a keyword, detect and predict where in an utterance the keyword occurs.
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing
In this paper, we first show that the per-utterance mean of CPC features captures speaker information to a large extent.
Direct multimodal few-shot learning of speech and images
We propose direct multimodal few-shot models that learn a shared embedding space of spoken words and images from only a few paired examples.
Unsupervised vs. transfer learning for multimodal one-shot matching of speech and images
Here we compare transfer learning to unsupervised models trained on unlabelled in-domain data.
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
The idea is to learn a representation of speech by predicting future acoustic units.
Ranked #1 on
Acoustic Unit Discovery
on ZeroSpeech 2019 English
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis.
