在杂的音频环境中基于变压器的语言独立的性别识别
Or Haim Anidjar1,2, Roi Yozevitch3,4
1Faculty of Computer Science, College of Management, Rishon Le'Tzion, Israel.
Scientific reports
|April 25, 2025
概括
这项研究引入了一种新的方法,用于在杂的音频中识别扬声器的性别. 梅尔光谱分析优于Wav2Vec2声学模型,在俄语中达到99%的准确性,并突出了公平的数据集需求.
科学领域:
- 语音处理 语音处理
- 机器学习 机器学习
- 计算语言学计算语言学
背景情况:
- 发言者性别识别对于语音识别系统至关重要.
- 由于训练数据不平衡,现有系统往往会表现出性别偏见.
- 噪音和语言变异在准确的性别检测中带来了重大挑战.
研究的目的:
- 开发一种独立的方法来从噪音环境中的音频片段中识别发言者的性别.
- 为了比较Mel光谱图与Wav2Vec2声学模型排放的有效性,用于性别识别.
- 通过确保均衡的数据集,解决和减轻语音识别中的性别偏见.
主要方法:
- 音频片段使用Mel光谱和Wav2Vec2声学模型排放进行处理.
- 在五种语言进行了实验:英语,阿拉伯语,西班牙语,法语和俄语.
- 一个平衡的数据集与相应的男性和女性音频剪辑被用来减轻偏差.
主要成果:
- 梅尔光谱法比Wav2Vec2变压器方法表现出更高的性能.
- 对俄语的谱图分析达到99%的准确性,而Wav2Vec2达到89%.
- 在不同语言和噪音/静音条件下训练的模型显示出更好的准确性.
结论:
- 梅尔光谱图为声学性别检测提供了强大的方法,在本研究中表现优于基于变压器的模型.
- 均衡的数据集对于减少语音识别系统中的性别偏见至关重要.
- 未来的系统应该包含多语言和多种环境数据,以提高可靠性和公平性.
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