For speech recognition in noisy environments, we propose a multi-task autoencoder which estimates not only clean speech but also noise from noisy speech. We introduce the deSpeeching autoencoder, which excludes speech signals from noisy speech, and combines it with the conventional denoising autoencoder to form a unified multi-task autoencoder (MTAE). We evaluate it using the Aurora 2 data set and 6-hour noise data set collected by ourselves. It reduced WER by 15.7% from the conventional denoising autoencoder in the Aurora 2 test set A.