## 4 Experimental Results and Analysis
### Verification for Automatic R-Vector Extraction
The results of experiments on the SiTEC corpus explain that the rhythm metric values extracted automatically by our proposed approach pursue a general tendency of two languages (English and Korean) reported on linguistics, providing a great expectation that the metrics convey language-discriminative information for automatic language identification. Next, in experiments on the Mozilla corpus, we investigated the properties of extracted rhythm metrics for other languages including Chinese, Spanish, and English. As shown in Table 4, Chinese-Spanish and Chinese-English pairs demonstrated significant properties of language discrimination of respective rhythm metrics, giving high significance levels. These two language pairs showed similar properties of rhythm metrics. Chinese represented higher values in %V and \(\Delta\)V than other languages, but which provided higher values in other metrics. In other words, Chinese is of considerable significance in discriminating vowel metrics, but its effect is moderated in normalized metrics. This result explains that the automatically extracted metrics provide relevant characteristics as language-discriminative features. On the other hand, the English-Spanish pair indicated lower significance levels except for nPVI-V. The unexpected results are caused by the phonemic models used for the detection of consonantal and vocalic intervals. Although the models trained with English data operated well for Korean (in Table 3) and Chinese (in Table 4), they might induce interval detection errors for Spanish, which has an opposite sound system to English. One possible solution is to construct phonemic models covering conflicting languages such as English and Spanish. Figure 7 summarizes the results of distribution of rhythm metrics evaluated with the Mozilla corpus. These results show similar tendencies to the significant results addressed in Table 4. Although overlapped quantiles are observed from three languages, most metrics indicated a distance between Chinese and other languages for median values. In comparison with vowel metrics, consonant metrics including \(\Delta\)C, Varco-C, and nPVI-C showed significant differences over languages, providing sufficient capability as language-discriminative features. Two metrics, Varco-V and nPVI-V, indicated highly \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \%V & \(\Delta\)V & \(\Delta\)C & **Varco-V** & **Varco-C** & **nPVI-V** & **rPVI-C** \\ \hline \multirow{2}{*}{CN—SP} & \(>\)1 & CN & CN & SP & SP & SP & SP \\  & Sig.2 & *** & *** & *** & *** & *** & *** & *** \\ \hline \multirow{2}{*}{CN—EN} & \(>\) & CN & CN & EN & EN & EN & EN & EN \\  & Sig. & *** & *** & *** & *** & *** & *** & *** \\ \hline \multirow{2}{*}{EN—SP} & \(>\) & EN & - & SP & - & EN & SP & SP \\  & Sig. & * & - & * & - & * & *** & * \\ \hline \hline \end{tabular}  
* A language with the higher value than the other;2 Significance level: \(<\) 0.001 ***, \(<\) 0.01 ***, \(<\) 0.05 ***.  
\end{table} 
Table 4: Significance results with LME for Chinese, Spanish, and English. Figure 6: Tendency of distribution of rhythm metric values for English and Korean. overlapped quantiles and similar median values on three languages, similarly to results analyzed for English and Korean.