The environmental robustness of DNN-based acoustic models can be significantly improved
by using multi-condition training data. However, as data collection is a costly proposition …

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 …

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 …

Recent studies on multi-microphone speech databases indicate that it is beneficial to
perform beamforming to improve speech recognition accuracies, especially when there is a …

In recent years, substantial progress has been made in the field of reverberant speech
signal processing, including both single-and multichannel dereverberation techniques and …

The processing of speech corrupted by interfering overlapping speakers is one of the
challenging problems with regards to today's automatic speech recognition systems …

Real-world audio recordings are often degraded by factors such as noise, reverberation,
and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to …

Existing efforts in audio adversarial attacks only focus on the scenarios where an adversary
has prior knowledge of the entire speech input so as to generate an adversarial example by …

We propose a method to generate audio adversarial examples that can attack a state-of-the-
art speech recognition model in the physical world. Previous work assumes that generated …

Self-supervised pretraining on speech data has achieved a lot of progress. High-fidelity
representation of the speech signal is learned from a lot of untranscribed data and shows …