Search Results for author:
Found 17 papers, 6 papers with code
sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks
Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient.
The NUS-HLT System for ICASSP2024 ICMC-ASR Grand Challenge
This paper summarizes our team's efforts in both tracks of the ICMC-ASR Challenge for in-car multi-channel automatic speech recognition.
A Refining Underlying Information Framework for Monaural Speech Enhancement
By bridging the speech enhancement and the Information Bottleneck principle in this letter, we rethink a universal plug-and-play strategy and propose a Refining Underlying Information framework called RUI to rise to the challenges both in theory and practice.
Selective HuBERT: Self-Supervised Pre-Training for Target Speaker in Clean and Mixture Speech
Self-supervised pre-trained speech models were shown effective for various downstream speech processing tasks.
Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation
Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available.
MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation
Recently, many deep learning based beamformers have been proposed for multi-channel speech separation.
VCSE: Time-Domain Visual-Contextual Speaker Extraction Network
In the first stage, we pre-extract a target speech with visual cues and estimate the underlying phonetic sequence.
MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources
These algorithms are usually achieved by mapping the multi-channel audio input to the single output (i. e. overall spatial pseudo-spectrum (SPS) of all sources), that is called MISO.
Language-specific Characteristic Assistance for Code-switching Speech Recognition
Dual-encoder structure successfully utilizes two language-specific encoders (LSEs) for code-switching speech recognition.
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent.
Iterative Sound Source Localization for Unknown Number of Sources
Sound source localization aims to seek the direction of arrival (DOA) of all sound sources from the observed multi-channel audio.
A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker Extraction
We propose a hybrid continuity loss function for time-domain speaker extraction algorithms to settle the over-suppression problem.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
L-SpEx: Localized Target Speaker Extraction
Speaker extraction aims to extract the target speaker's voice from a multi-talker speech mixture given an auxiliary reference utterance.
USEV: Universal Speaker Extraction with Visual Cue
The speaker extraction algorithm requires an auxiliary reference, such as a video recording or a pre-recorded speech, to form top-down auditory attention on the target speaker.
Multi-stage Speaker Extraction with Utterance and Frame-Level Reference Signals
Speaker extraction requires a sample speech from the target speaker as the reference.
SpEx+: A Complete Time Domain Speaker Extraction Network
To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+.
Ranked #1 on
Speech Extraction
on WSJ0-2mix-extr
Speech Extraction
Audio and Speech Processing
Sound
