  Inferring emotion status from users' queries plays an important role to
enhance the capacity in voice dialogues applications. Even though several
related works obtained satisfactory results, the performance can still be
further improved. In this paper, we proposed a novel framework named
multi-channel auto-encoder (MTC-AE) on emotion recognition from acoustic
information. MTC-AE contains multiple local DNNs based on different low-level
descriptors with different statistics functions that are partly concatenated
together, by which the structure is enabled to consider both local and global
features simultaneously. Experiment based on a benchmark dataset IEMOCAP shows
that our method significantly outperforms the existing state-of-the-art
results, achieving $64.8\%$ leave-one-speaker-out unweighted accuracy, which is
$2.4\%$ higher than the best result on this dataset.
