## 4 Conclusions and Discussion  
Through acoustic experiments and machine learning methods, this study systematically explores the differences in vowel pronunciation features and their acoustic mechanisms among Mandarin learners at different proficiency levels, providing theoretical and technical support for pronunciation quality assessment and teaching strategy optimization. Based on 5880 speech samples from 50 learners (advanced, intermediate, and elementary groups) and 10 standard speakers, vowel formant parameters (F1-F2) are extracted to construct an acoustic space model, and SVM and ID3 algorithms are employed for cross-level classification analysis. The results show that Support Vector Machine significantly outperforms the ID3 algorithm in vowel classification tasks, with an average classification accuracy of 92.09% for the three learner groups, which is an improvement of 2.05 percentage points (\(p\) < 0.001) compared to ID3, owing to its efficient modeling ability for high-dimensional acoustic features. Acoustic space analysis further reveals the systematic deviation characteristics of learners' pronunciation: the elementary group exhibits significant deviations \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vowel**} & **UCA Deviation** & **UCI Deviation** & **UCB Deviation** \\  & **(Bark)** & **(Bark)** & **(Bark)** \\ \hline a & 0.09 & 0.08 & 0.11 \\ o & 0.26 & 0.19 & 0.12 \\ r & 0.47 & 0.40 & 0.43 \\ u & 0.14 & 0.38 & 0.52 \\ y & 0.18 & 0.06 & 0.61 \\ i & 0.16 & 0.14 & 1.01 \\ z & 0.09 & 0.28 & 2.61 \\ \(\chi\) & 0.11 & 0.10 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculation results of vowel acoustic deviation. in complex vowels (such as the apical vowel \"z\" with a deviation of 2.61 Bark), while the advanced group's deviations are generally less than 0.5 Bark (e.g., the deviation of vowel \"a\" is only 0.09 Bark), and the degree of deviation is significantly negatively correlated with Mandarin proficiency test scores (r = \(-\)0.76, \(p\) < 0.01). This finding is consistent with the acoustic-perceptual correlation theory, confirming the sensitivity of formant parameters in pronunciation quality assessment. This study innovatively proposes a collaborative analysis framework of acoustic features and machine learning, providing quantitative evidence for the development of real-time pronunciation evaluation modules in CALL systems. By precisely locating pronunciation error types (such as tongue position deviation and insufficient lip shape control), this framework can facilitate the design of personalized teaching plans, while also holding important reference value for the optimization of acoustic features in multi-ethnic Mandarin speech recognition technology. Future research will expand the sample to learners from various dialect backgrounds and incorporate dynamic acoustic parameters (such as formant trajectories and prosodic features) to further enhance the model's universality and practicality. While SVM and ID3 are effective for static formant classification, their lack of adaptability to dynamic speech patterns limits broader applications. Future work will explore advanced techniques like convolutional neural networks (CNNs) and integrate dynamic acoustic features (e.g., formant trajectories and prosodic patterns) to enhance robustness in real-world scenarios. This framework, combining acoustic features with machine learning, provides a foundation for real-time pronunciation evaluation modules in Computer-Assisted Language Learning (CALL) systems. By identifying specific error types (e.g., tongue position deviation, lip shape control), it supports personalized teaching plans and offers reference value for optimizing multi-ethnic Mandarin speech recognition technologies. Future research will also expand sample diversity (e.g., dialect backgrounds) to improve model universality. Investigation, H.Y. ; project administration, G.A. ; resources, G.A. ; supervision, G.A. ; validation, G.A. ; writing--original draft, H.Y. ; data curation, T.A. ; data curation, H.Y.