There may indeed be some unequal treatment of language recognition technology among people in non-English speaking countries, which is mainly reflected in the following aspects: 1. **Data bias**: Most language recognition systems are trained mainly based on English data, so they are superior to other languages in terms of accuracy and effectiveness. Support for non-English languages often requires more corpora and data, but these resources may not be very abundant, resulting in low recognition rates. 2. **Accents and dialects**: Even for the same language, the diversity of regional accents and dialects will affect the accuracy of recognition. For example, some dialects may not be fully trained, which will cause the system to fail to understand or recognize correctly. 3. **Cultural and contextual differences**: Language is not just vocabulary and grammar, but also carries cultural background and context. Many times, language recognition systems perform poorly when processing expressions in a specific cultural context, resulting in misunderstandings or recognition errors. 4. **Technology development and resource allocation**: Due to market demand and technology development priorities, many technology companies may give priority to the development of English-related technologies, while research and development of non-English languages is relatively insufficient, which leads to technological inequality. 5. **User experience**: Due to the above reasons, users in non-English speaking countries may face more difficulties and inconveniences in the user experience of using language recognition technology, which will affect their use. In summary, language recognition technology does treat users in non-English speaking countries and regions unequally to some extent, but with the continuous development and diversification of technology, related problems are expected to be improved.