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With the widespread use of adaptive multi-rate (AMR) speech-based applications, AMR speech-based steganography has witnessed significant growth. Consequently, steganalysis approaches have garnered attention to mitigate network security risks associated with AMR speech-based steganography. However, existing studies often assume known embedding rates of test samples, leaving steganography detection under unknown embedding rates—an encountered practical scenario—unresolved. To tackle this challenge, this paper presents a novel detection scheme for AMR speech-based steganography, skillfully combining clustering and ensemble learning. The training phase utilizes K-means clustering to pre-classify speech samples, grouping them into distinct clusters based on their feature distribution and embedding rates. Subsequently, a classifier based on extreme gradient boosting (XGBoost) is trained …