## 6 Conclusion and Future Tasks  
This paper discusses the specialty of pronunciation learning in language learning and emphasizes the importance of auditory cognitive ability in the second language. It takes Chinese tone learning as an example and considers the cognitive load and the shortage of the general learning method's pronunciation practice by imitating the model voices of words, phrases, or sentences. Against this background, we focused on polysyllable perceptual training, emphasized the comparison of each tone, and designed tasks commensurate with the learner's cognitive ability using the small step strategy. Furthermore, we considered the shortness of sensory memory and optimized the interface and operation design of the perceptual training application, compared with the imitation practice app. As a result, the tone pronunciation correct answer rate of users was 30% to 36% higher in two basic pronunciation tasks and 12% higher in the phrase pronunciation task after use for a total of three hours. In the results of the paired sample t-test, there was a statistically significant difference in the correct answer rate of the pronunciation tasks mentioned above from learning with the imitation practice app and the perceptual training application (\(t(9)=5.78\), \(p<0.001\), [PERSON]'s \(d=1.83\), \(t(9)=6.22\), \(p<0.001\), [PERSON]'s \(d=1.97\) and \(t(9)=3.33\), \(p=0.009\), [PERSON]'s \(d=1.05\)).  
However, the implementation of the tone pronunciation practice function did not help perceptual training app users to improve their tone pronunciation ability. Furthermore, practicing pronunciation could be the cause of tonal pronunciation errors. Therefore, we considered using machine learning to support tone pronunciation practice. We aimed to use machine learning to design pronunciation tasks rather than recognize tones correctly, as is the case in existing research. Furthermore, in addition to the prediction results of the trained model, the percentage of classification, the training data set, and the visualized data were presented to the learner as feedback for learning. We concluded that machine-learning-assisted tone pronunciation practice is feasible.  
In the future, it is necessary to consider the instructional design of pronunciation practice based on the difficulty of tone combination, error patterns, phonemes, etc. Furthermore, it is also necessary to optimize the interfaces and operation design or to use cloud computing to minimize the computing time and extraneous cognitive load. On the other hand, there are studies showing that the individual differences between people and the data of language processing (in Mandarin Chinese) can be used to predict people's ability, which helps in understanding human cognition and creates navigation by machine learning [36; 37]. Classifying learners automatically according to their phonological ability and providing navigation considering their individual differences using machine learning are also future issues.  
Conceptualization, M.S.K. and A.I.; methodology, M.S.K. and A.I.; software, M.S.K.; validation, M.S.K.; formal analysis, M.S.K. and A.I.; investigation, M.S.K. and A.I.; resources, A.I.; data curation, M.S.K. and A.I.; writing--original draft preparation, M.S.K.; writing--review and editing, A.I.; visualization, M.S.K.; supervision, A.I.; project administration, A.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
I would like to express my gratitude to the students who participated in the experiments.  
The authors declare no conflict of interest.