Zhengyang Chen

Automatic Mobile Application Traffic Identification by Convolutional Neural Networks

2016

Mobile network security and management are becoming important issues, due to the rapid development and widespread of the mobile network. Application traffic identification is a critical technology to resolve these issues. A variety of traffic …

Large-Scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification

2022 · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The speech representations learned from large-scale unlabeled data have shown better generalizability than those from supervised learning and thus attract a lot of interest to be applied for various downstream tasks. In this paper, we …

Self-Supervised Speaker Verification Using Dynamic Loss-Gate and Label Correction

2022 · arXiv (Cornell University)

For self-supervised speaker verification, the quality of pseudo labels decides the upper bound of the system due to the massive unreliable labels. In this work, we propose dynamic loss-gate and label correction (DLG-LC) to alleviate …

Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit

2022 · arXiv (Cornell University)

Speaker modeling is essential for many related tasks, such as speaker recognition and speaker diarization. The dominant modeling approach is fixed-dimensional vector representation, i.e., speaker embedding. This paper introduces a research and production oriented speaker …

Exploring Binary Classification Loss For Speaker Verification

2023 · arXiv (Cornell University)

The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric learning-based objectives depend strongly on searching effective pairs which might hinder further …

Target Speech Detection With Multimodal Prompts

2025 · IEEE Transactions on Audio Speech and Language Processing

Traditional speaker diarization seeks to detect “who spoke when” according to speaker characteristics. Extending to target speech detection, we detect “when target speech event occurs” according to the semantic characteristics of speech. We propose a …