8 citations · 22 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech
Juliette Millet, Jean-Remi King
Our ability to comprehend speech remains, to date, unrivaled by deep learning models. This feat could result from the brain's ability to fine-tune generic sound representations for…
Perceptimatic: A human speech perception benchmark for unsupervised subword modelling
Juliette Millet, Ewan Dunbar
In this paper, we present a data set and methods to compare speech processing models and human behaviour on a phone discrimination task. We provide Perceptimatic, an open data set…
The Perceptimatic English Benchmark for Speech Perception Models
Juliette Millet, Ewan Dunbar
We present the Perceptimatic English Benchmark, an open experimental benchmark for evaluating quantitative models of speech perception in English. The benchmark consists of ABX sti…
Learning to detect dysarthria from raw speech
Juliette Millet, Neil Zeghidour
Speech classifiers of paralinguistic traits traditionally learn from diverse hand-crafted low-level features, by selecting the relevant information for the task at hand. We explore…