The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

Sequence-to-sequence models have been widely used in end-to-end speech processing,
for example, automatic speech recognition (ASR), speech translation (ST), and text-to …

Recently great strides have been made in the field of automatic speech recognition (ASR) by
using various deep learning techniques. In this study, we present a thorough comparison …

Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …

In recent years, wsj0-2mix has become the reference dataset for single-channel speech
separation. Most deep learning-based speech separation models today are benchmarked …

We introduce Wavesplit, an end-to-end source separation system. From a single mixture, the
model infers a representation for each source and then estimates each source signal given …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

Despite the growing interest in unsupervised learning, extracting meaningful knowledge
from unlabelled audio remains an open challenge. To take a step in this direction, we …