Perceptual and featural measures of Mandarin consonant similarity: Confusion matrices and phonological features dataset
This article presents a comprehensive dataset containing two types of similarity measures for 23 Mandarin consonant phonemes: perceptual and featural measures. The perceptual measures are derived from confusion matrices obtained through native speakers’ identification tasks in quiet and noise-masked...
Gespeichert in:
Veröffentlicht in: | Data in brief 2024-02, Vol.52, p.109868, Article 109868 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | This article presents a comprehensive dataset containing two types of similarity measures for 23 Mandarin consonant phonemes: perceptual and featural measures. The perceptual measures are derived from confusion matrices obtained through native speakers’ identification tasks in quiet and noise-masked conditions. Specific perceptual measures, including confusion rate and perceptual distance, are calculated based on these matrices. Additionally, a phonological feature system is proposed to evaluate the featural differences between each pair of consonants, providing insights into phonological similarity. The dataset reveals a significant positive correlation between the perceptual and featural measures of similarity. Furthermore, distance matrices are generated using the perceptual distance data, and a hierarchical cluster dendrogram is plotted using the unweighted pair group method with arithmetic mean (UPGMA). The dendrogram shows five major clusters of consonants. Future studies can refer to this dataset for quantified perceptual measures of Mandarin consonant similarity. This dataset can also be valuable for future research exploring consonant similarity in perceptual and phonological domains, as well as investigating the influence of linguistic and extralinguistic factors on consonant perception. |
---|---|
ISSN: | 2352-3409 2352-3409 |
DOI: | 10.1016/j.dib.2023.109868 |