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 …

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 …

We review autoregressive predictive coding (APC), an approach to learn speech
representation by predicting a future frame given the past frames. We present three different …

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 …

The study of non-human animals' communication systems generally relies on the
transcription of vocal sequences using a finite set of discrete units. This set is referred to as a …

This paper addresses the significant challenge of predicting the Remaining Useful Life
(RUL) of mechanical equipment, a critical aspect of predictive maintenance and reliability …

Although the relationship between prejudice and predictive coding has attracted more
attention recently, many important issues remain to be investigated, such as why prejudice is …

Visually grounded models learn from paired images and their spoken captions. Recently,
there have been attempts to utilize the visually grounded models trained from images and …

This paper presents a novel Dialectal Sound and Vowelization Recovery framework,
designed to recognize borrowed and dialectal sounds within phonologically diverse and …

Self-supervised methods such as Contrastive predictive Coding (CPC) have greatly
improved the quality of the unsupervised representations. These representations …