USTC-NELSLIP System Description for DIHARD-III Challenge

• URL: http://arxiv.org/abs/2103.10661v1
• Date: Fri, 19 Mar 2021 07:00:51 GMT
• Title: USTC-NELSLIP System Description for DIHARD-III Challenge
• Authors: Yuxuan Wang, Maokui He, Shutong Niu, Lei Sun, Tian Gao, Xin Fang, Jia Pan, Jun Du, Chin-Hui Lee
• Abstract summary: The innovation of our system lies in the combination of various front-end techniques to solve the diarization problem. Our best system achieved DERs of 11.30% in track 1 and 16.78% in track 2 on evaluation set.
• Score: 78.40959509760488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This system description describes our submission system to the Third DIHARD Speech Diarization Challenge. Besides the traditional clustering based system, the innovation of our system lies in the combination of various front-end techniques to solve the diarization problem, including speech separation and target-speaker based voice activity detection (TS-VAD), combined with iterative data purification. We also adopted audio domain classification to design domain-dependent processing. Finally, we performed post processing to do system fusion and selection. Our best system achieved DERs of 11.30% in track 1 and 16.78% in track 2 on evaluation set, respectively.

Related papers

• TCG CREST System Description for the Second DISPLACE Challenge [19.387615374726444]
  We describe the speaker diarization (SD) and language diarization (LD) systems developed by our team for the Second DISPLACE Challenge, 2024. Our contributions were dedicated to Track 1 for SD and Track 2 for LD in multilingual and multi-speaker scenarios.
  arXiv  Detail & Related papers  (2024-09-16T05:13:34Z)
• MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition [62.89464258519723]
  We propose a multi-layer cross-attention fusion based AVSR approach that promotes representation of each modality by fusing them at different levels of audio/visual encoders. Our proposed approach surpasses the first-place system, establishing a new SOTA cpCER of 29.13% on this dataset.
  arXiv  Detail & Related papers  (2024-01-07T08:59:32Z)
• TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization [54.41494515178297]
  We reformulate speaker diarization as a single-label classification problem. We propose the overlap-aware EEND (EEND-OLA) model, in which speaker overlaps and dependency can be modeled explicitly. Compared with the original EEND, the proposed EEND-OLA achieves a 14.39% relative improvement in terms of diarization error rates.
  arXiv  Detail & Related papers  (2023-03-08T05:05:26Z)
• Audio-visual multi-channel speech separation, dereverberation and recognition [70.34433820322323]
  This paper proposes an audio-visual multi-channel speech separation, dereverberation and recognition approach. The advantage of the additional visual modality over using audio only is demonstrated on two neural dereverberation approaches. Experiments conducted on the LRS2 dataset suggest that the proposed audio-visual multi-channel speech separation, dereverberation and recognition system outperforms the baseline.
  arXiv  Detail & Related papers  (2022-04-05T04:16:03Z)
• Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge [4.022057598291766]
  Royalflush speaker diarization system submitted to the Multi-channel Multi-party Meeting Transcription Challenge. System comprises speech enhancement, overlapped speech detection, speaker embedding extraction, speaker clustering, speech separation and system fusion.
  arXiv  Detail & Related papers  (2022-02-10T03:35:05Z)
• The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap [67.395341302752]
  This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of the five subsystems: two x-vector-based subsystems, two end-to-end neural diarization-based subsystems, and one hybrid subsystem.
  arXiv  Detail & Related papers  (2021-02-02T07:30:44Z)
• The HUAWEI Speaker Diarisation System for the VoxCeleb Speaker Diarisation Challenge [6.6238321827660345]
  This paper describes system setup of our submission to speaker diarisation track (Track 4) of VoxCeleb Speaker Recognition Challenge 2020. Our diarisation system consists of a well-trained neural network based speech enhancement model as pre-processing front-end of input speech signals.
  arXiv  Detail & Related papers  (2020-10-22T12:42:07Z)
• Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning [49.41766997393417]
  The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, dataaugmentation, multi-task learning, and post-processing, for audiocaptioning. The system received the highest evaluation scores, but which of the individual elements most fully contributed to its perfor-mance has not yet been clarified.
  arXiv  Detail & Related papers  (2020-09-24T01:07:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.