###### Abstract
[7] extracts deep acoustic features from raw speech signals through self-supervised learning and demonstrates superior performance in cross-linguistic speech recognition tasks. This opens up possibilities for integrating traditional acoustic parameters (such as formants) with deep features. In the area of multilingual vowel classification, [PERSON] et al. [8] found significant categorical differences in cross-accent vowel perception through a comparative study of five English dialects, which highlights the urgency of combining acoustic features with perceptual evaluation. In terms of acoustic feature analysis, traditional research often relies on formant parameters. [PERSON] and [PERSON] [9] revealed the clustering characteristics of vowel categories through the F1-F2 acoustic space modeling, and this theory has been widely applied to second language pronunciation error detection. Recent studies have further expanded this paradigm: [PERSON] et al. [10] proposed an improved contextual speech representation method, which enhances the modeling ability for tonal languages by incorporating prosodic features, and [PERSON] et al. [11] used convolutional neural networks (CNN) to achieve end-to-end classification of Chinese tones, with an accuracy of 92.3%, validating the potential of deep learning in speech feature extraction. Additionally, [12; 13] qualitatively analyzed the relationship between formant changes and vowel phonemes and tongue positions in their research. Study [14] provides a more detailed explanation of formant changes in vowels. The authors of [15] also mention that formant movement trajectories are effective parameters for vowel recognition. Study [16] uses formants as parameters to analyze the variation trends of vowels in eight languages, including Arabic, English, Mandarin Chinese, and French. On this basis, the authors of [17] propose an objective testing method for the pronunciation level of finals in Mandarin Chinese based on formant patterns, using formants as pronunciation evaluation features and SVM as the classifier. [PERSON] et al. [18]systematically compared the performance of algorithms such as SVM and KNN in vowel recognition and found that SVM has a significant advantage in high-dimensional acoustic feature classification (with a 12% improvement in F1 score), which is consistent with the conclusions of this study. However, existing research tends to focus on optimizing individual algorithms and lacks systematic evaluation across different levels of learners. For example, the KNN-SVM hybrid model proposed by [PERSON] [19] performs excellently in isolated word recognition but does not consider the hierarchical differences in learners' pronunciation levels. Furthermore, the authors of [20] employ a phoneme classifier to assess learners' pronunciation. The phoneme classifier is trained using speech data from native speakers of the target language and uses mel-frequency cepstral coefficients as features. If the learner's pronunciation is close to the trained speech data, the classifier's discriminative power will be high. Prosodic feature models are adopted in [21; 22], which use segment duration models and speaking rate models to evaluate learners' pronunciation quality. The authors of [23] propose a pronunciation quality assessment model based on acoustic features for second language learners' vowel pronunciation problems, emphasizing the importance of formant parameters in non-native pronunciation analysis. The authors of [24] provide a detailed summary of the classification performance of SVM, further validating its effectiveness in processing acoustic feature data. However, existing research primarily focuses on the performance analysis of single algorithms and lacks a systematic exploration of vowel classification for learners at different proficiency levels. In the field of CALL, the intelligent analysis of acoustic features provides new ideas for the development of pronunciation evaluation systems.