密集-Fusion2Net是一个更高效和轻量级的短语语音识别系统,具有时频频道注意力
Fei Deng1, Rui Huang2, Peifan Jiang3
1College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, Sichuan, China.
Scientific reports
|March 21, 2025
概括
本研究介绍了密集融合2网和时频频道注意力 (TFCA),以改善短语演讲的扬声器识别. 新系统提高了性能和稳定性,即使数据和噪声有限.
科学领域:
- 语音处理 语音处理
- 机器学习 机器学习
- 生物识别信息 生物识别信息
背景情况:
- 扬声器识别系统由于有限的数据和噪音,难以处理短语段.
- 现有的方法往往无法有效地从简短的发言中提取演讲者的关键信息.
研究的目的:
- 开发一个强大的扬声器识别系统,专门用于短语段.
- 改进声学特征的利用,增强全球特征提取能力.
主要方法:
- 提出了一种新的网络架构,Dense-Fusion2Net,用于在短语中高效地利用功能.
- 引入了时间频道注意力 (TFCA),以捕捉跨时间,频率和频道的相互依赖.
- 在Voxceleb数据集上进行实验,以验证系统性能.
主要成果:
- 与TFCA相结合的Dense-Fusion2Net在简短的语音识别中表现出卓越的性能和稳定性.
- 实验确定了处理短语段的最佳窗口长度.
- 拟议的系统有效地解决了现有的短语演讲者识别的局限性.
结论:
- 密集融合2网架构和TFCA注意力机制显著提升了短语演讲者的识别.
- 这些发现为在音频数据有限的场景中识别扬声器提供了更可靠的解决方案.
- 进一步的研究可以探索不同声环境的最佳参数调.
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