Abstract
AbstractPrevious research has suggested that the production of speech rhythm in a second language (L2) or foreign language is influenced by the speaker’s first language rhythm. However, it is less clear how the production of L2 rhythm is affected by the learners’ L2 proficiency, largely due to the lack of rhythm metrics that show consistent results between studies. We examined the production of English rhythm by 75 Korean learners with the rhythm metrics proposed in previous studies (pairwise variability indices and interval measures). We also devised new sentence stress measures (i.e., accentuation rate and accentuation error rate) and investigated whether these new measures can quantify rhythmic differences between the learners. The results found no rhythm metric that significantly correlated with proficiency in the expected direction. In contrast, we found a significant correlation between the learners’ proficiency levels and both measures of sentence stress, showing that less-proficient learners placed sentence stress on more words and made more sentence stress errors. This demonstrates that our measures of sentence stress can be used as effective features for assessing Korean learners’ English rhythm proficiency.
Highlights
Spoken languages have been classified into stress-timed, syllable-timed, and moratimed languages
Chen and Zechner (2011) did not find any significant correlation between rhythm scores and proficiency levels for non-native speakers of English whose L1 was Mandarin, adding the rhythm metrics to their automatic English speech scoring system significantly improved the performance of the system compared to a model that was only based on nonrhythm features
The limitations of the rhythm metrics have been previously reported (e.g., Arvaniti, 2012; Wiget et al, 2010), we investigated whether the rhythm metrics can capture rhythmic differences between Korean learners of English in a large-scale study and compared these with the metrics that we will propose in this study
Summary
Spoken languages have been classified into stress-timed, syllable-timed, and moratimed languages. Arvaniti (2012) tested the performance of the rhythm metrics (ΔC, %V, rPVI, nPVI, VarcoC, and VarcoV) on English, German, Greek, Italian, Spanish, and Korean Her results showed that the metric scores were affected by various types of “noise” in the data such as elicitation methods (e.g., read or spontaneous speech), the complexity of syllable structure, and interspeaker variation. Singapore English and Hong Kong English were found to have smaller variability in syllable durations than British English as measured by the rhythm metrics, due to the influence of Singaporean Mandarin and Cantonese, respectively (Deterding, 2001; Low et al, 2000; Setter, 2006) It remains uncertain whether the rhythm metrics can be used to evaluate the rhythmic characteristics of non-native learners with varying levels of proficiency. Chen and Zechner (2011) did not find any significant correlation between rhythm scores and proficiency levels for non-native speakers of English whose L1 was Mandarin, adding the rhythm metrics to their automatic English speech scoring system significantly improved the performance of the system (i.e., higher agreement between machine-predicted scores and human scores) compared to a model that was only based on nonrhythm features
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