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 “ẓ” 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.

The concept of ‘global village’ has linked all countries in the world as a whole. The exchanges and cooperation between different countries in politics, economy, culture, and education are becoming more and more frequent. To master a language, the study of its pronunciation is crucial. Based on the above considerations, this paper uses the Chinese pronunciation corpus of 30 learners and 10 Chinese native speakers as basic data to discuss the pronunciation patterns of learners’ mandarin diphthongs and their similarities to standard pronunciations. In this paper, by using the theory and methods of experimental phonetics, comparative analysis and error analysis, we exam and analyse the acoustic features of different levels of learners in the learning process, and the pronunciation and bias of learners’ mandarin diphthongs are obtained. From the experimental results, it can be seen that all three groups of learners have some difficulties in pronunciation of mandarin diphthongs. In particular, the learners have the greatest differences to standard pronunciations when pronouncing the diphthongs [ua] and [uo]. And some important rules and conclusions are summarised through experiments. This provides an important reference for learners to solve the problems of diphthongs pronunciation in Chinese speech synthesis and recognition.

This study addresses the problem of Chinese pronunciation errors made by native Uyghur speakers by proposing a phoneme confusion modeling framework that integrates cross-linguistic phonological comparison and data-driven methods. Based on speech transfer theory, a corpus of 52,510 standard and non-standard pronunciations (total duration 167.2 hours) was constructed. The dynamic time warping (DTW) algorithm was employed to align phoneme sequences, and Apriori association rule mining was used to uncover high-frequency confusion patterns. The study found that differences in sound, like the lack of retroflex consonants and the simplification of compound vowels, caused systematic errors. For example, the high-frequency substitution of affricates “ch→q” (62.5%) and compound vowels “uo→o/u” (76.1%). Data-driven methods further validated the phonological hypotheses and identified context-dependent phenomena (such as “ian→an” medial weakening). The confusion rule base achieved a robustness of F1-score 0.87. The quantitative model based on the confusion matrix can be integrated into intelligent speech assessment systems to provide real-time corrective feedback (e.g., “‘uo’ detected as misread as ‘o’, probability 76.1%”). This study provides an interpretable rule base and dynamic alignment technical foundation for cross-language pronunciation error correction algorithms, which has theoretical and practical significance for optimizing computer-assisted language teaching systems.

The rapid globalization of regions with different languages necessitates more advanced non-native tongue proficiency among individuals who need to communicate across language barriers. To deal with this demand, the research conducts a comparative analysis of the acoustic features of similar sounds between standard Mandarin (L2) and Mandarin spoken by Uyghurs (L1). Flege’s Speech Learning Model as well as Lado’s Contrastive Analysis Hypothesis serve as the foundation for this study. First, an acoustic analysis of Mandarin Chinese vowels produced via Uyghur and native Mandarin speakers is carried out. Ten Mandarin speakers and ten Uyghur Chinese speakers were compared with respect to word-level productions, and the 1st, 2nd formant frequencies (F1, F2) for Mandarin vowels were studied. The comparative acoustic analysis results show that the production of four ‘similar’ vowels by male and female Uyghur Chinese speakers differs significantly from native Mandarin speakers. Second, the following features are seen when native Mandarin speakers and Uyghur Chinese speakers compare their vowel spaces: Uyghur speakers’ vowels have a lower F1, which means that their overall vowel space distribution is surpassing that of Chinese speakers’. Thirdly, the Euclidean Distance analysis of monophthongs produced via Uyghur as well as Mandarin Chinese speakers reveals a gender-specific applicability of the Speech Learning Model. While the model successfully explains the production of Mandarin monophthongs by Uyghur female speakers, it falls short in accounting for the production patterns observed in Uyghur male speakers. The research findings of this thesis contribute a new direction to future developments in research aimed at improving speech recognition and Computer-Assisted Language Learning (CALL) systems.