The CHiME challenge series aims to advance far field speech recognition technology by
promoting research at the interface of signal processing and automatic speech recognition …

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 presents the design and outcomes of the CHiME-3 challenge, the first open
speech recognition evaluation designed to target the increasingly relevant multichannel …

The performance of automatic speech recognition systems under noisy environments still
leaves room for improvement. Speech enhancement or feature enhancement techniques for …

The CHiME challenge series has been aiming to advance the development of robust
automatic speech recognition for use in everyday environments by encouraging research at …

We propose a multi-style learning (multi-style training+ deep learning) procedure that relies
on deep denoising autoencoders (DAEs) to extract and organize the most discriminative …

Scores from traditional confidence classifiers (CCs) in automatic speech recognition (ASR)
systems lack universal interpretation and vary with updates to the underlying confidence or …

In this age of pervasive computing, numerous scientific accomplishments, such as artificial
intelligence and machine learning [ML], have conveyed exciting uprisings to human …

In this investigation, a deep neural network (DNN) based speech extraction method is
proposed to enhance a speech signal propagating from the desired direction. The proposed …

In order to achieve an utterance classifier that not only works robustly against speech
recognition errors but also maintains high accuracy for input with no errors, we propose the …