CAs-Net:维吾尔语语音识别的通道感知语音网络
Jiang Zhang1, Miaomiao Xu1, Lianghui Xu1,2
1School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了通道意识语音网络 (CAS-Net),以增强低资源语音识别,特别是噪音环境中的维吾尔语. CAs-Net显著提高了准确性,实现了5.72%的文字错误率 (WER).
科学领域:
- 语音识别 语言识别
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 低资源语音识别在有限的数据和噪音条件下面临着挑战.
- 现有的模型难以有效处理复杂的声环境和语言细微差别.
研究的目的:
- 开发一个先进的语音识别模型,CAS-Net,以提高在低资源和噪音情况下的性能.
- 增强语音识别系统的上下文建模和时间模式识别能力.
主要方法:
- 拟议的通道识别语音网络 (CAS-Net) 具有通道旋转模块 (CIM) 和多尺度深度卷积模块 (MSDCM).
- CIM重建了空间结构建模的通道向量;MSDCM在变压器框架内捕获了多尺度的时间模式.
- 使用多分支深度可分离的卷积和轻量级的自我注意力机制.
主要成果:
- 在维吾尔语语音识别数据集上,CAS-Net取得了最佳表现.
- 在文字错误率 (WER) 中显著降低,平均达到5.72%.
- 在具有挑战性的条件下表现优于现有的语音识别方法.
结论:
- 在杂的环境中,CAS-Net对于低资源的语音识别已经被证明是有效和强大的.
- 拟议的模块增强了上下文理解和时间特征提取.
- 该模型显示了改善代表性不足的语言语音识别的巨大潜力.
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