Advances in speech technologies have led to significant progress in large acoustic models such as Whisper and Multilingual Massive Speech (MMS), improving tasks like Automatic Speech Recognition (ASR). Yet, there is still a need for thorough research to recognize and tackle stereotypical biases. In this paper, we investigate Whisper and MMS systems to quantify gender bias and factorize gender bias considering voice timbre, skin tone, and age group for Mexican-Spanish in a multilingual ASR setting. In addition to traditional ASR evaluation such as word error rate and phoneme error rate, we also perform statistical significance tests. Furthermore, we explore the vital role of factorization of gender attributes into sub-groups in bias quantification. This work presents an initial study of gender inclusivity with various factors in the context of MMS and Whisper for Mexican-Spanish.