DeLiang Wang

TL;DR: Systematic evaluation shows that the proposed system extracts a majority of unvoiced speech without including much interference, and it performs substantially better than spectral subtraction.

TL;DR: A supervised speech separation system that significantly improves automatic speech recognition (ASR) performance in realistic noise conditions is presented and a framework that unifies separation and acoustic modeling via joint adaptive training is proposed.

TL;DR: This work proposes a supervised approach using regression trees to learn the nonlinear transformation of the uncertainty from the linear spectral domain to the cepstral domain, which is used by a decoder that exploits the variance associated with the enhanced cEPstral features to improve robust speech recognition.

TL;DR: A new VAD algorithm based on boosted deep neural networks (bDNNs) is described that outperforms state-of-the-art VADs by a considerable margin and employs a new acoustic feature, multi-resolution cochleagram (MRCG), that concatenates the cochreagram features at multiple spectrotemporal resolutions and shows superior speech separation results over many acoustic features.

TL;DR: Substantial sentence-intelligibility benefit was observed for hearing-impaired listeners in both noise types, despite the use of unseen noise segments during the test stage, which highlights the importance of evaluating these algorithms not only in human subjects, but in members of the actual target population.