用词典和词典下线索对儿童进行语音转录的单词细分
Zébulon Goriely1, Andrew Caines1, Paula Buttery1
1Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.
Journal of child language
|September 12, 2023
概括
一个新的语音细分框架,DYnamic Programming MULTIple-cue框架 (DYMULTI),结合了词汇和词汇下线索. DYMULTI取得了最先进的结果,在针对儿童的语言交叉实验中表现优于现有的模型.
科学领域:
- 计算语言学计算语言学
- 心理语言学 心理语言学
- 语音处理 语音处理
背景情况:
- 现有的针对儿童语音的单词细分框架,PHOCUS和MULTICUE,代表了不同的语音处理方法.
- PHOCUS依赖于词汇识别,而MULTICUE使用子词汇属性进行边界决定.
研究的目的:
- 为了比较PHOCUS和MULTICUE的性能.
- 开发一种新的,综合的语音细分框架,结合两种方法的优势.
- 评估新框架在多种语言中的有效性.
主要方法:
- 对PHOCUS和MULTICUE框架的复制和基准测试.
- 开发动态编程多个cue框架 (DYMULTI).
- 在26种语言中对DYMULTI与PHOCUS和MULTICUE进行了跨语言评估.
主要成果:
- 无论是PHOCUS还是MULTICUE,都取得了具有竞争力的细分结果.
- 迪穆尔蒂展示了最先进的性能.
- 在跨语言实验中,DYMULTI在26种语言中的15种语言中表现优于PHOCUS和MULTICUE.
结论:
- DYMULTI有效地整合了词汇和子词汇线索,以实现强大的语音细分.
- 该框架在跨语言环境中的成功证实了其心理语言学的基础.
- DYMULTI对理解语言获取和语音处理做出了重大贡献.
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