42 citations · 94 across the 8 of their papers we have counts for
13 papers · 1 filter
Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge
Ewan Dunbar, Nicolas Hamilakis, Emmanuel Dupoux
Recent progress in self-supervised or unsupervised machine learning has opened the possibility of building a full speech processing system from raw audio without using any textual…
Are word boundaries useful for unsupervised language learning?
Tu Anh Nguyen, Maureen de Seyssel, Robin Algayres +3
Word or word-fragment based Language Models (LM) are typically preferred over character-based ones in many downstream applications. This may not be surprising as words seem more li…
Predicting non-native speech perception using the Perceptual Assimilation Model and state-of-the-art acoustic models
Juliette Millet, Ioana Chitoran, Ewan Dunbar
Our native language influences the way we perceive speech sounds, affecting our ability to discriminate non-native sounds. We compare two ideas about the influence of the native la…
Do self-supervised speech models develop human-like perception biases?
Juliette Millet, Ewan Dunbar
Self-supervised models for speech processing form representational spaces without using any external labels. Increasingly, they appear to be a feasible way of at least partially el…
The Zero Resource Speech Challenge 2021: Spoken language modelling
Ewan Dunbar, Mathieu Bernard, Nicolas Hamilakis +6
We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the…
Paraphrases do not explain word analogies
Louis Fournier, Ewan Dunbar
Many types of distributional word embeddings (weakly) encode linguistic regularities as directions (the difference between "jump" and "jumped" will be in a similar direction to tha…