## 5 Discussion
### Learning Mandarin Tone with Machine Learning  
In many cases, artificial intelligence (AI) refers to machine learning or neural networks that refer to human cognitive structures. For foreign language pronunciation learning, there are some cases that recommend using AI, such as speech recognition systems, which can be used for practicing conversation in foreign languages. Speech recognition can grade learners' pronunciation through speech analysis of the acoustic features, etc. However, the answers calculated using these technologies and AI may be wrong, or the accuracy rate may be significantly reduced depending on the purpose of the system. Using a speech recognition conversational system to practice tone pronunciation, it is likely that the correct tones and tonal errors will be ignored to make a conversation. In the studies of Chinese tone recognition using machine learning, the tone of monosyllabe words was used as training data, and the method and accuracy of tone recognition were examined. The highest accuracy (95.53%) was obtained using the convolutional neural network (CNN) and the mel-frequency cepstral coefficients (MFCCs) for tone pronunciation of 4500 syllables by 125 children aged 3 to 10. This study [34] showed that tone recognition by machine learning is possible; however, there are some shortcomings in learning tones with the tone recognition system. This study used children's speech, which had limitations on the pitch range, and the spectrogram and MFCCs of pronounced monosyllabic words in the paper showed that the third tone was a dipping-rising tone (Figure 4). In related works, as mentioned in Section 2.2, training with disyllabic words is more effective than training with monosyllabic words [23], and teaching the dipping-rising tone may cause pronunciation errors [24]. Furthermore, as the tones are made up of differences in relative pitch patterns, we considered that it is necessary to design pronunciation tasks that promote tone categorization of learners. Figure 4: Machine learning data from previous studies on tone recognition [34]. Top: Time waveforms; Middle: Spectrograms; Bottom: Mel-frequency cepstral coefficients (MFCCs). A study [35] that used the training data fetched and selected from the pronunciation of 590 short sentences by two pairs of men and women obtained an accuracy of 87.6% using a CNN. The accuracy of tone recognition is strongly related to the data used for machine learning, and we aimed to design pronunciation tasks that improve the abilities required for tone pronunciation rather than correctly recognizing tones. At the first stage of learning tone, we consider that learners should acquire the cognitive ability of tones, then learners should practice pronunciation with vocal cords and their tonal cognition. We used the designed voices as training data and made a pronunciation exercise using a CNN. The designed task for the pronunciation exercise was a disyllabic task that requested the pronunciation of tones 1, 2, 3, and 4 in the first syllable combined with tone 1 in the second syllable within 1.1 seconds and the length of the first syllable in less than 0.55 seconds. The data used for machine learning (CNN) were pronunciation data (a total of 547 items of audio, 60% for training and 40% for validation) of four men and three women who followed the rules of the pronunciation task for the training and test set. The classification accuracy was 98.18%, which was high due to the data pattern selection by rules and the features of the two-syllable tone. Figure 5 shows a part of the data used for machine learning. We considered that we should make tone learners from the machine learning aware of the processes of how a CNN classifies different tone pairs and the results of the classification by percentage. Learners can obtain key information to acquire tones from the process of machine learning, learning  
Figure 5: Training data for the pronunciation task designed in this study.