一个基于深度学习的可调整强制调整系统:对儿童言语的应用
Prad Kadambi1,2, Tristan J Mahr3, Katherine C Hustad4
1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe.
Journal of speech, language, and hearing research : JSLHR
|March 31, 2025
概括
一个新的工具,Wav2TextGrid,可以为儿童的言语进行可训练的语音对齐,比基线方法提高了准确性. 这个系统直接训练手动对齐,增强临床级语音分析.
科学领域:
- 语音处理 语音处理
- 计算语言学计算语言学
- 语音学 语音学 语音学
背景情况:
- 语音强制对齐对于自动语音分析至关重要,特别是对于儿童语音等非标准语音.
- 手动对齐是黄金标准,但耗时,目前的工具不支持对这些对齐进行直接训练.
研究的目的:
- 开发一个可训练的,适应扬声器的语音强制对齐系统,用于儿童的言语.
- 为了提高准确度,可以直接培训手动对齐.
主要方法:
- 开发了Wav2TextGrid,一种神经强制对齐器,使用了42名神经类型儿童 (3-6岁) 的身体.
- 评估儿童语言表现和TIMIT语料库,以评估跨年龄和方言的适应能力.
主要成果:
- 与基线相比,Wav2TextGrid工具显著提高了所有语音类别的对齐精度.
- 在儿童的言语中实现了超过40%的精度改善,用于儿童的语音和语音.
- 将现有方法与13分钟的标记数据相匹配;在45-60分钟内看到显著的改进.
结论:
- Wav2TextGrid提供了一个替代的对齐工作流程,将强制对齐定制为与临床级手动对齐相匹配.
- 该工具增强了对儿童语音和其他非标准语音模式的分析.
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