音频增强用于通过歧视性学习识别非母语儿童的语音识别
1School of Electronics Engineering, VIT-AP University, Amaravati 522237, India.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究通过开发特征空间歧视模型来增强非本地儿童的自动语音识别 (ASR). 速度扰乱数据增强显著改善了儿童学习第二语言的ASR性能.
科学领域:
- 人与计算机的互动.
- 语音处理 语音处理
- 获取第二语言的获取
背景情况:
- 儿童与虚拟助手的互动日益增加,推动了自动语音识别 (ASR) 的进步.
- 非母语儿童在第二语言 (L2) 获取过程中表现出独特的语言错误,挑战目前的ASR系统.
- 现有的ASR很难准确地识别非本土儿童的语音模式.
研究的目的:
- 为非母语儿童开发一个有效的自动语音识别系统.
- 通过解决L2获取语音特征来提高ASR性能.
- 调查L2熟练程度对儿童ASR系统的影响.
主要方法:
- 使用特征空间歧视模型,包括特征空间最大相互信息 (fMMI) 和增强的特征空间最大相互信息 (fbMMI).
- 实现了基于速度干扰的数据增强,用于儿童的语音体.
- 收集和分析的语音数据涵盖了各种说话风格,包括阅读和自发言语.
主要成果:
- 与传统的ASR基线模型相比,特征空间MMI模型显示出更高的性能.
- 增加速度扰动因子与改进的ASR准确性正相关.
- 开发的系统显示了对非母语儿童语音的增强识别能力.
结论:
- 与速度扰动相结合的特征空间歧视模型为非本地儿童的ASR提供了有希望的方法.
- 解决L2语言获取语言中的特定挑战对于强大的ASR系统至关重要.
- 这项研究有助于为年轻学习者提供更具包容性和有效性的人机交互技术.
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