Search Results for author:
Found 11 papers, 5 papers with code
DiaCorrect: Error Correction Back-end For Speaker Diarization
In this work, we propose an error correction framework, named DiaCorrect, to refine the output of a diarization system in a simple yet effective way.
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism
SqueezeFormer has recently shown impressive performance in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
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.
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.
INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing
The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing.
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.
