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20212024
most citedA Real-time Speaker Diarization System Based on Spatial Spectrum

17 citations · 19 across the 11 of their papers we have counts for

collaborators

10 papers

eess.AS2024

Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision

Yafeng Chen, Siqi Zheng, Hui Wang +4

Training speaker-discriminative and robust speaker verification systems without explicit speaker labels remains a persisting challenge. In this paper, we propose a new self-supervi…

eess.AS2022

Contextual Expressive Text-to-Speech

Jianhong Tu, Zeyu Cui, Xiaohuan Zhou +4

The goal of expressive Text-to-speech (TTS) is to synthesize natural speech with desired content, prosody, emotion, or timbre, in high expressiveness. Most of previous studies atte…

cs.SD20221 cited

Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis

Zhihao Du, Shiliang Zhang, Siqi Zheng +1

Recently, hybrid systems of clustering and neural diarization models have been successfully applied in multi-party meeting analysis. However, current models always treat overlapped…

eess.AS2022

Deep Representation Decomposition for Rate-Invariant Speaker Verification

Fuchuan Tong, Siqi Zheng, Haodong Zhou +3

While promising performance for speaker verification has been achieved by deep speaker embeddings, the advantage would reduce in the case of speaking-style variability. Speaking ra…

cs.SD2022

Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure

Siqi Zheng, Hongbin Suo

Clustering-based speaker diarization has stood firm as one of the major approaches in reality, despite recent development in end-to-end diarization. However, clustering methods hav…

eess.AS2022

Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data

Fuchuan Tong, Siqi Zheng, Min Zhang +4

Unsupervised clustering on speakers is becoming increasingly important for its potential uses in semi-supervised learning. In reality, we are often presented with enormous amounts…