语音基础模型可以有效地识别低资源的多语言老龄化人口中的语言吗?
Aditya Kommineni1, Rajat Hebbar1, Sarah Petrosyan2
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, USA.
JASA express letters
|September 11, 2025
概括
语音基础模型 (SFMs) 显示,语音输入较短和非母语使用者的表现下降. 较大的模型和少量学习提高了老龄化,多语言人口的性能.
科学领域:
- 计算语言学 计算语言学
- 语音技术 语言技术
- 老年学是一门学科.
背景情况:
- 语音基础模型 (SFM) 在各种任务中表现出色,但它们对老年人,多语言语音的有效性尚未得到充分理解.
- 使用口语识别作为代理任务来评估这个人口中的SFM.
研究的目的:
- 调查SFM对老年人多语言语言表达的表现.
- 确定为服务于老龄化人口的SFM的关键品质:耐久性,人口不变性和短暂的可转移性.
主要方法:
- 用老年人,多语言语言的语言识别的SFMs的零射击评估.
- 短暂的学习实验,以评估不同模型大小的可转移性.
- 基于输入时间和演讲者出生程度的性能变化分析.
主要成果:
- 在零射击评估中,对于较短的语音输入观察到显著的性能下降.
- 在所有语言中,母语使用者的演讲总是比非母语使用者取得更好的结果.
- 短暂的学习证明了更好的可转移性,特别是在更大的SFM架构中.
结论:
- SFM需要进一步发展,以有效地处理老年人的细微差别,多语言的演讲.
- 对输入时间的稳定性和扬声器的原生性是改善的关键领域.
- 较大的模型和少量学习策略表明,在老龄化人口的低资源场景中,增强SFM性能具有前途.
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