一种基于上下文的方法来预测中断噪音中的有意义和无意义单词的可理解性:模型评估
Jelmer van Schoonhoven1, Koenraad S Rhebergen2, Wouter A Dreschler1
1Department of Clinical and Experimental Audiology, Amsterdam University Medical Center, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
The Journal of the Acoustical Society of America
|October 20, 2023
概括
基于上下文的扩展语音传输指数 (cESTI) 模型显示了语音可理解性预测的改进,特别是在调整了前向掩盖的情况下. 这种改进的模型在各种噪音条件下更好地预测单词的可理解性.
科学领域:
- 听力学 听力学是指听力学.
- 语音感知 语音感知
- 声学 声学 在声学上
背景情况:
- 基于上下文的扩展语音传输指数 (cESTI) 模型预测了噪音中的语音可理解性.
- 之前的研究表明,cESTI的有效性与有意义的单音节单词.
- 该模型在不同的上下文程度和词类型的表现仍然不清楚.
研究的目的:
- 评估cESTI模型在预测静止和中断噪音中的有意义和无意义单词的语音可理解性方面的表现.
- 调查不同噪声中断率和前向掩盖对可理解性预测的影响.
- 改进cESTI模型以提高准确性.
主要方法:
- 在静止和中断的噪音条件下,测量了14名正常听力成年人的有意义和无意义的CVC单词的语音可理解性.
- 将cESTI模型的预测能力与原来的ESTI模型进行了比较.
- 调整了模型参数,特别是前置掩盖,以提高预测准确度.
主要成果:
- cESTI模型解释了无意义词可理解性的75%的差异,超过了原来的ESTI模型 (27%的差异).
- 对于有意义的词分数的预测最初很差 (38%的差异),原因是中断率低和前向掩盖的问题.
- 调整前掩盖参数显著提高了cESTI模型的准确性,解释了83%的总差异.
结论:
- cESTI模型显示了语音可理解性的增强预测能力,特别是在完善前向掩盖参数之后.
- 精细的cESTI模型提供了更准确的语音可理解性评估在不同的噪音条件和词类型.
- 进一步的研究可能会探索该模型在不同听力场景和人群中的应用.
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