<p>This paper presents the UCI Phonotactic Calculator (UCIPC), a new online tool for quantifying the occurrence of segments and segment sequences in a corpus. This tool has several advantages compared to existing tools: it allows users to supply their own training data, meaning it can be applied to any language for which a corpus is available; it computes a wider range of metrics than most existing tools; and it provides an accessible point-and-click interface that allows researchers with more modest technical backgrounds to take advantage of phonotactic models. After describing the metrics implemented by the calculator and how to use it, we present the results of a proof-of-concept study comparing how well different types of metrics implemented by the UCIPC predict human responses from eight published nonce word acceptability judgment studies across four different languages. These results suggest that metrics that take into account the relative position of sounds and include word boundaries are better at predicting human responses than those that are based on the absolute position of sounds and do not include word boundaries. We close by discussing the usefulness of tools like the UCIPC in experimental design and analysis and outline several areas of future research that this tool will help support.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

The UCI Phonotactic Calculator: An online tool for computing phonotactic metrics

  • Connor Mayer,
  • Arya Kondur,
  • Megha Sundara

摘要

This paper presents the UCI Phonotactic Calculator (UCIPC), a new online tool for quantifying the occurrence of segments and segment sequences in a corpus. This tool has several advantages compared to existing tools: it allows users to supply their own training data, meaning it can be applied to any language for which a corpus is available; it computes a wider range of metrics than most existing tools; and it provides an accessible point-and-click interface that allows researchers with more modest technical backgrounds to take advantage of phonotactic models. After describing the metrics implemented by the calculator and how to use it, we present the results of a proof-of-concept study comparing how well different types of metrics implemented by the UCIPC predict human responses from eight published nonce word acceptability judgment studies across four different languages. These results suggest that metrics that take into account the relative position of sounds and include word boundaries are better at predicting human responses than those that are based on the absolute position of sounds and do not include word boundaries. We close by discussing the usefulness of tools like the UCIPC in experimental design and analysis and outline several areas of future research that this tool will help support.