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With the development of speech steganography technology, steganographers are more and more inclined to realize more secure covert communication by combining a series of steganography methods. Thus, this letter presents a novel multi-encoder network (MENet) to achieve more efficient detection of multiple steganography methods. Differing from the previous work, MENet utilizes multiple private encoders to individually model the private features of each coding element, introduces a shared encoder based on an attention mechanism to fuse multiple private features for achieving better feature representation, and finally exploits a shared decoder to reduce feature dimensionality as well as give predictions. Taking the existing state-of-the-art steganography methods as the detection targets, the performance of the proposed steganalysis method is evaluated comprehensively and compared with the state-of-the-art …