47 citations · 201 across the 13 of their papers we have counts for
6 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…
Evaluating context-invariance in unsupervised speech representations
Mark Hallap, Emmanuel Dupoux, Ewan Dunbar
Unsupervised speech representations have taken off, with benchmarks (SUPERB, ZeroSpeech) demonstrating major progress on semi-supervised speech recognition, speech synthesis, and s…
Toward a realistic model of speech processing in the brain with self-supervised learning
Juliette Millet, Charlotte Caucheteux, Pierre Orhan +5
Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely…
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…