We propose TF-GridNet for speech separation. The model is a novel deep neural network
(DNN) integrating full-and sub-band modeling in the time-frequency (TF) domain. It stacks …

We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating in the time-
frequency (TF) domain, for monaural talker-independent speaker separation in anechoic …

Neural codecs have become crucial to recent speech and audio generation research. In
addition to signal compression capabilities, discrete codecs have also been found to …

We propose DiffSep, a new single channel source separation method based on score-
matching of a stochastic differential equation (SDE). We craft a tailored continuous time …

Self-supervised learning representation (SSLR) has demonstrated its significant
effectiveness in automatic speech recognition (ASR), mainly with clean speech. Recent work …

Neural speech separation has made remarkable progress and its integration with automatic
speech recognition (ASR) is an important direction towards realizing multi-speaker ASR …

As the quality of contemporary speech synthesis improves, so too does the interest from
language communities in develo** text-to-speech (TTS) systems for a variety of real-world …

This paper describes our submission to the Second Clarity Enhancement Challenge
(CEC2), which consists of target speech enhancement for hearing-aid (HA) devices in noisy …

The performance of speaker verification (SV) models may drop dramatically in noisy
environments. A speech enhancement (SE) module can be used as a front-end strategy …

The vast majority of the world's languages are unable to follow in the footsteps of existing
resource-intensive pathways to building text-to-speech (TTS) systems. But, as the quality of …