VB-Adapter:用于跨域语音表示学习的变化贝叶斯适配器
概括
本研究引入了一个变化的贝叶斯适配器 (VB-Adapter),用于在遇到不熟悉的语音域时改进语音识别模型. VB-Adapter通过有效地管理由域位移引起的不确定性,从而提高模型的稳定性.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 由于广泛的预训练,当前的语音模型可以很好地泛化.
- 预训练和微调数据之间的域名转移对现实世界的语音场景提出了挑战.
- 不熟悉的语音数据可能会导致现有模型的性能下降.
研究的目的:
- 提出一种在微调过程中跨领域语音表示学习的新方法.
- 为了解决语音识别领域转移造成的性能差距.
- 在遇到新型语音数据时增强语音模型的稳定性.
主要方法:
- 开发了一个变量贝叶斯适配器 (VB-Adapter),采用隐性变量模型.
- 构建一个后端分布来弥合源代码和目标域间隙.
- 引入了一个适应性目标,最大限度地提高相互信息和对比学习的优化.
主要成果:
- VB-Adapter在患有关节障碍的语音识别 (DSR) 中表现出有效性.
- 应用于口哨编码器和Llama的普通话语音识别 (MSR),显示了显著的改进.
- 该方法成功地模拟了由域位移引起的不确定性.
结论:
- 在跨域情景中,VB-Adapter提高了语音表示的稳定性.
- 拟议的方法有效地减轻了由于域转移而导致的性能下降.
- 这项工作为调整预训练的语音模型适应各种现实应用提供了一个有希望的解决方案.
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