H-VECTORS: Improving the robustness in utterance-level speaker embeddings using a hierarchical attention model-Reference-Cited by-同舟云学术

H-VECTORS: Improving the robustness in utterance-level speaker embeddings using a hierarchical attention model

Published:2021-10 Issue: Volume:142 Page:329-339
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Shi Yanpei^ORCID,Huang Qiang,Hain Thomas^ORCID

Funder

Innovate UK

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference59 articles.

1. Callhome american english speech;Alexandra Canavan,2001

2. Bahdanau, D., Cho, K., & Bengio, Y.

3. Bai, Z., Zhang, X.-L., & Chen, J. Speaker recognition based on deep learning: An overview. arXiv preprint arXiv:2012.00931.

4. Cai, W., Chen, J., & Li, M. Exploring the encoding layer and loss function in end-to-end speaker and language recognition system. arXiv preprint arXiv:1804.05160.

5. A method of estimating the equal error rate for automatic speaker verification;Cheng,2004

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Emotion embedding framework with emotional self-attention mechanism for speaker recognition;Expert Systems with Applications;2024-03

2. Multi-level attention network: Mixed time–frequency channel attention and multi-scale self-attentive standard deviation pooling for speaker recognition;Engineering Applications of Artificial Intelligence;2024-02

3. End-to-end neural speaker diarization with an iterative adaptive attractor estimation;Neural Networks;2023-09

4. End-to-end deep speaker embedding learning using multi-scale attentional fusion and graph neural networks;Expert Systems with Applications;2023-07

5. Multi-level Feature Joint Learning Methods for Emotional Speaker Recognition;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18