During times of crisis, effective communication is essential for ensuring that accurate and timely information is disseminated through disaster management systems. One important element of this communication is the use of speech interfaces to convey crucial messages. However, these systems face challenges in diverse and ever-changing disaster scenarios due to differences in speakers' characteristics. That's why speaker adaptation techniques are crucial for improving the performance and resilience of speech-based disaster management systems. In this research paper, we present a thorough comparative analysis of different speaker adaptation methods currently utilized in disaster management systems. These techniques include speaker normalization, feature transformation, and deep neural network-based approaches. Finally we propose a model with basic components which are required to build a speech based disaster management systems.