Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

In late fusion, each modality is processed in a separate unimodal Convolutional Neural
Network (CNN) stream and the scores of each modality are fused at the end. Due to its …

A field that has directly benefited from the recent advances in deep learning is automatic
speech recognition (ASR). Despite the great achievements of the past decades, however, a …

The CHiME challenge series aims to advance robust automatic speech recognition (ASR)
technology by promoting research at the interface of speech and language processing …

The CHiME challenges have played a significant role in the development and evaluation of
robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR …

Public evaluation campaigns and datasets promote active development in target research
areas, allowing direct comparison of algorithms. The second edition of the challenge on …

Speech enhancement and separation are core problems in audio signal processing, with
commercial applications in devices as diverse as mobile phones, conference call systems …

Speech enhancement and automatic speech recognition (ASR) are most often evaluated in
matched (or multi-condition) settings where the acoustic conditions of the training data …

This paper reports the organization and results for the 2018 community-based Signal
Separation Evaluation Campaign (SiSEC 2018). This year's edition was focused on audio …

In this article, we present an account of the state of the art in acoustic scene classification
(ASC), the task of classifying environments from the sounds they produce. Starting from a …