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 …

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 …

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

In this paper, we extend the deep long short-term memory (DL-STM) recurrent neural
networks by introducing gated direct connections between memory cells in adjacent layers …

The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The
2014 evaluation offered multiple tracks on lecture transcription and translation based on the …

This paper considers acoustic beamforming for noise robust automatic speech recognition
(ASR). A beamformer attenuates background noise by enhancing sound components …

Standard deep neural network-based acoustic models for automatic speech recognition
(ASR) rely on hand-engineered input features, typically log-mel filterbank magnitudes. In this …

CHiME-3 is a research community challenge organised in 2015 to evaluate speech
recognition systems for mobile multi-microphone devices used in noisy daily environments …

We investigate convolutional neural networks (CNNs) for large vocabulary distant speech
recognition, trained using speech recorded from a single distant microphone (SDM) and …

This work presents a broad study on the adaptation of neural network acoustic models by
means of learning hidden unit contributions (LHUC)—a method that linearly re-combines …