Search Results for author:
Found 15 papers, 3 papers with code
SEF-PNet: Speaker Encoder-Free Personalized Speech Enhancement with Local and Global Contexts Aggregation
Personalized speech enhancement (PSE) methods typically rely on pre-trained speaker verification models or self-designed speaker encoders to extract target speaker clues, guiding the PSE model in isolating the desired speech.
UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023
Our final system is a fusion of six models and achieves the first place in Track 1 and second place in Track 2 of VoxSRC 2023.
Multi-pass Training and Cross-information Fusion for Low-resource End-to-end Accented Speech Recognition
Moreover, we propose to train the Aformer in a multi-pass manner, and investigate three cross-information fusion methods to effectively combine the information from both general and accent encoders.
Dynamic Acoustic Compensation and Adaptive Focal Training for Personalized Speech Enhancement
Recently, more and more personalized speech enhancement systems (PSE) with excellent performance have been proposed.
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system
Exploiting effective target modeling units is very important and has always been a concern in end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DiaCorrect: End-to-end error correction for speaker diarization
In recent years, speaker diarization has attracted widespread attention.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Heterogeneous Separation Consistency Training for Adaptation of Unsupervised Speech Separation
SCT follows a framework using two heterogeneous neural networks (HNNs) to produce high confidence pseudo labels of unlabeled real speech mixtures.
Selective Pseudo-labeling and Class-wise Discriminative Fusion for Sound Event Detection
In recent years, exploring effective sound separation (SSep) techniques to improve overlapping sound event detection (SED) attracts more and more attention.
PercepNet+: A Phase and SNR Aware PercepNet for Real-Time Speech Enhancement
Finally, we propose to integrate the loss of complex subband gain, SNR, pitch filtering strength, and an OA loss in a multi-objective learning manner to further improve the speech enhancement performance.
DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction
Particularly, we find that the Mixture-Remix fine-tuning with DPCCN significantly outperforms the TD-SpeakerBeam for unsupervised cross-domain TSE, with around 3. 5 dB SISNR improvement on target domain test set, without any source domain performance degradation.
Improving Channel Decorrelation for Multi-Channel Target Speech Extraction
Moreover, new combination strategies of the CD-based spatial information and target speaker adaptation of parallel encoder outputs are also investigated.
CNN-based Discriminative Training for Domain Compensation in Acoustic Event Detection with Frame-wise Classifier
Domain mismatch is a noteworthy issue in acoustic event detection tasks, as the target domain data is difficult to access in most real applications.
Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection
A good joint training framework is very helpful to improve the performances of weakly supervised audio tagging (AT) and acoustic event detection (AED) simultaneously.
Multi-channel target speech extraction with channel decorrelation and target speaker adaptation
In this work, we propose two methods for exploiting the multi-channel spatial information to extract the target speech.
Speech Extraction
Audio and Speech Processing
Attention-based scaling adaptation for target speech extraction
Furthermore, by introducing a mixture embedding matrix pooling method, our proposed attention-based scaling adaptation (ASA) can exploit the target speaker clues in a more efficient way.
