###### Abstract  
Based on acoustic feature analysis, this study systematically examines the differences in vowel pronunciation characteristics among Mandarin learners at various proficiency levels. A speech corpus containing samples from advanced, intermediate, and elementary learners (N = 50) and standard speakers (N = 10) was constructed, with a total of 5880 samples. Support Vector Machine (SVM) and ID3 decision tree algorithms were employed to classify vowel formant parameters (F1-F2) patterns. The results demonstrate that SVM significantly outperforms the ID3 algorithm in vowel classification, with an average accuracy of 92.09% for the three learner groups (92.38% for advanced, 92.25% for intermediate, and 91.63% for elementary), an improvement of 2.05 percentage points compared to ID3 (\(p<0.05\)). Learners' vowel production exhibits systematic deviations, particularly pronounced in complex vowels for the elementary group. For instance, the apical vowel \"z\" has a deviation of 2.61 Bark (standard group: F1 = 3.39/F2 = 8.13; elementary group: F1 = 3.42/F2 = 10.74), while the advanced group's deviations are generally less than 0.5 Bark (e.g., vowel \"a\" deviation is only 0.09 Bark). The difficulty of tongue position control strongly correlates with the deviation magnitude (\(r=0.87\), \(p<0.001\)). This study confirms the effectiveness of objective assessment methods based on formant analysis in speech acquisition research, provides a theoretical basis for algorithm optimization in speech evaluation systems, and holds significant application value for the development of Computer-Assisted Language Learning (CALL) systems and the improvement of multi-ethnic Mandarin speech recognition technology. M +
Footnote †: journal: Applied sciences  
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language of China's 56 ethnic groups. Promoting Mandarin is conducive to enhancing the unity among various ethnic groups in our country, facilitating exchanges between different regions, strengthening the cohesion and centripetal force of the Chinese nation, and holds great significance for our country's social development [2]. However, mastering a communicative language, particularly oral language learning, is of utmost importance. Vowels are the basic units that constitute speech sounds, and their correct pronunciation is essential for improving the quality of language communication. For non-standard Mandarin learners, accurately mastering the vowel pronunciation in Mandarin is also a challenge. CALL [3; 4; 5] systems have emerged in this context. CALL systems utilize computers to assist (or replace) humans in traditional speech teaching, aiming to promote students' autonomous learning by providing speech instruction and addressing their pronunciation issues in oral language. Therefore, Mandarin learners eagerly anticipate such automatic detection methods for Mandarin pronunciation errors that can precisely identify learners' pronunciation problems, propose corresponding improvement suggestions and measures, and further enhance students' Mandarin pronunciation level and ability, just like a teacher [6]. Consequently, the evaluation and classification of Mandarin learners' vowel pronunciation quality is an important research direction in the fields of phonetics and computer-assisted language teaching. With the rapid development of acoustic analysis techniques and machine learning algorithms, significant progress has been made in both theoretical methods and practical applications. However, due to the complexity of the Mandarin vowel system and the diversity of learners' pronunciation proficiency levels, accurately and efficiently evaluating and classifying learners' vowel pronunciation quality remains a challenging issue. In recent years, numerous scholars have conducted extensive research on learners' pronunciation assessment and vowel classification. In recent years, breakthroughs in speech representation learning technologies have provided a new paradigm for pronunciation assessment. The wav2 vec 2.0 framework proposed by [PERSON] et al.