We present a method for visually-grounded spoken term discovery. After training either a
HuBERT or wav2vec2. 0 model to associate spoken captions with natural images, we show …

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
improving performance and data efficiency on various speech tasks. However, these …

The task of phone-to-audio alignment has many applications in speech research. Here we
introduce two Wav2Vec2-based models for both text-dependent and text-independent …

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 …

Recent work on unsupervised speech segmentation has used self-supervised models with
phone and word segmentation modules that are trained jointly. This paper instead revisits …

Unsupervised representation learning for speech processing has matured greatly in the last
few years. Work in computer vision and natural language processing has paved the way, but …

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
producing performance and data efficiency improvements for a variety of speech tasks …

The success of deep learning comes from its ability to capture the hierarchical structure of
data by learning high-level representations defined in terms of low-level ones. In this paper …

Transformers achieve unrivalled performance in modelling language, but remain inefficient
in terms of memory and time complexity. A possible remedy is to reduce the sequence …

Compressing self-supervised models has become increasingly necessary, as self-
supervised models become larger. While previous approaches have primarily focused on …