Clear communication in public address systems is essential, especially in environments where safety or information clarity is critical. Speech intelligibility is often assessed using objective intelligibility metrics (OIMs), which predict intelligibility through mathematical models. These metrics perform well in non-highly reverberant conditions but face challenges in highly reverberant environments and with non-European languages like Mandarin. This study examines the performance of three intrusive OIMs—ESTOI, HASPI, and SIIB\textsubscript{Gauss}—in two aspects: (1) how these metrics perform under different reverberation conditions for English, using STIPA as a reference, and (2) how robust these metrics are by comparing the variances of scores between Mandarin and English. The results show that the variances of predicted scores by the test metrics are equal between Mandarin and English. HASPI, ESTOI, and SIIB\textsubscript{Gauss} demonstrate similar performance across a broader range of reverberation conditions (from a T60 of 0.05s to 7s) for English, contradicting the theory that most intrusive intelligibility metrics struggle with severe reverberation conditions \cite{galbrun_speech_2016}. The findings highlight the need for further research to evaluate potential biases in OIMs and their performance across languages. Incorporating listening tests could provide a more solid examination of these metrics under diverse conditions for different languages.