Language recognition technology may indeed lead to some degree of unequal treatment of people from non-English speaking countries. This inequality may stem from the following aspects: 1. **Data imbalance**: The training data of many language recognition technologies mainly comes from English or other languages with a large population, which may lead to higher recognition and understanding accuracy for these languages, and poor recognition performance for languages with a small population or fewer resources. 2. **Technical resource investment**: Large technology companies may prioritize optimizing resources for languages with a large number of users and a large market, which may lead to insufficient development and optimization of small languages. 3. **Cultural and contextual understanding**: Language recognition involves not only the understanding of vocabulary and grammar, but also the understanding of culture and context. Language processing for non-English speaking countries often requires deeper knowledge of social and cultural background, which is more complicated in technical implementation. 4. **Bias and discrimination**: Due to factors such as the selection of training data sets and the reflection of social biases, language recognition technology may inadvertently amplify certain social biases and treat users of certain languages or dialects unfairly. In order to reduce these inequalities, many research institutions and companies are working to expand the diversity of their data sets and improve support for multiple languages and dialects. This requires international cooperation and more resources to ensure fairness and inclusiveness of language recognition technology.