作为短段非线性特征的重复图片嵌入,用于使用空气,骨和喉麦克风进行多式扬声器识别
K Khadar Nawas1, A Shahina2, Keshav Balachandar2
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India.
Scientific reports
|May 31, 2024
概括
本研究介绍了重复图 (RP) 嵌入作为说话者识别的新型,非线性特征. 这些嵌入式有效地捕捉了独特的声道动态,以便在不同语音传输模式中准确识别扬声器.
科学领域:
- 语音处理和声学学
- 生物系统中的非线性动力学
- 机器学习用于生物识别.
背景情况:
- 语音的产生涉及到一个非线性声道系统 (VT).
- 扬声器特征通常使用线性光谱特征来建模.
- 视听系统的非线性动态在扬声器识别方面还没有得到充分的研究.
研究的目的:
- 建议重复图 (RP) 嵌入作为独立的,非线性扬声器歧视特征.
- 评估RP嵌入式在不同语音传输模式 (空气,骨头,喉) 中用于扬声器识别的有效性.
- 评估使用RP嵌入式的单模和多模系统的性能.
主要方法:
- 使用了两个数据集:TIMIT语音语料库和一个辅音-母音单调音节数据集.
- 作为非线性特征的使用循环图 (RP) 嵌入.
- 使用单模 (空气,骨头,喉) 和多模 (双模,三模) 系统进行了闭幕扬声器识别实验.
主要成果:
- 在RP嵌入式上训练的单模系统实现了高精度:空气 (95.81%),骨头 (98.18%) 和喉 (99.74%).
- 最好的三模系统 (空气-骨-喉) 达到99.84%的准确率,与使用光谱图和MFCCs的系统相美.
- 双模骨喉系统实现了98.84%的准确性,在没有空气导通的情况下证明了有效性.
结论:
- RP嵌入是显著的非线性特征,能够独立识别扬声器.
- 声道系统 (VT) 的非线性动力学,通过RP嵌入来捕捉,是高度扬声器特定的.
- 这种非线性特征表示具有推动扬声器和语音识别技术的潜力.
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