Language recognition technology may bring some challenges to people in non-English-speaking countries to a certain extent, but it cannot be simply attributed to unequal treatment.： **Challenging aspects**： 1. **Data deviation**: For a long time, Chinese and English data have accounted for a relatively large proportion of the training data of language recognition technology. This may lead to the recognition accuracy of non-English languages is not as good as English, especially for some niche languages. For example, some unique languages of Africa, Asia, and South America may face poor recognition accuracy in technical applications. 2. **Language diversity issues**: There are many types of languages in non-English-speaking countries, and there are relatively few users of each language. With limited resources and energy, it may be difficult for technology developers to fully optimize all non-English languages. **Reasons for not completely unequal treatment**： 1. **Limitations on the stage of technological development**: Language recognition technology is still developing continuously. With the increase of attention and investment in multilingual data, the ability to recognize non-English languages is gradually improving. For example, some research institutions and companies have begun to build data sets and optimize algorithms specifically for mainstream non-English languages in different regions. 2. **Application scenario considerations**: On a global scale, English is indeed one of the most widely used languages, occupying a dominant position in many fields such as international business and scientific and technological exchanges. From the perspective of application needs, more English data is reasonable. 3. **Positive improvement trend**: More and more forces are aware of the importance of language equality, promoting the development of language recognition technology in a fairer and more widely applicable direction, and striving to reduce the adverse effects of language differences.