arXiv 2409.102402024-09-16eess.AScs.SD

oboVox 远场说话人识别：一种结合预训练模型的新型数据增强方法

oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models

Department of Electrical \& Electronic Engineering, Rajshahi University of Engineering \& Technology

Muhammad Sudipto Siam Dip, Md Anik Hasan, Sapnil Sarker Bipro, Md Abdur Raiyan, Mohammod Abdul Motin

备注：: 5 pages, 2 figures

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AI总结：

本研究提出一种向注册文件加噪声的新型数据增强技术，结合多种预训练模型开展远场说话人识别实验，验证了resnet模型的性能优势及该增强技术对识别效果的提升作用，助力RoboVox远场说话人识别任务。

摘要：

本研究针对说话人识别挑战，提出了一种向注册文件添加噪声的新型数据增强技术。该技术能有效对齐测试文件与注册文件的来源，提升二者的可比性。研究采用了多种预训练模型，其中resnet模型取得了最高的0.84 DCF（检测代价函数）和13.44%的EER（等错误率）。应用该增强技术后，resnet模型的结果显著提升至0.75 DCF和12.79% EER。对比分析显示，resnet的性能优于ECPA、Mel-spectrogram、Payonnet及Titanet large等模型。上述结果结合不同的增强方案，为本文中RoboVox远场说话人识别的成功实现提供了支撑。

英文摘要：

In this study, we address the challenge of speaker recognition using a novel data augmentation technique of adding noise to enrollment files. This technique efficiently aligns the sources of test and enrollment files, enhancing comparability. Various pre-trained models were employed, with the resnet model achieving the highest DCF of 0.84 and an EER of 13.44. The augmentation technique notably improved these results to 0.75 DCF and 12.79 EER for the resnet model. Comparative analysis revealed the superiority of resnet over models such as ECPA, Mel-spectrogram, Payonnet, and Titanet large. Results, along with different augmentation schemes, contribute to the success of RoboVox far-field speaker recognition in this paper

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