  As a general means of expression, audio analysis and recognition has
attracted much attentions for its wide applications in real-life world. Audio
emotion recognition (AER) attempts to understand emotional states of human with
the given utterance signals, and has been studied abroad for its further
development on friendly human-machine interfaces. Distinguish from other
existing works, the person-dependent patterns of audio emotions are conducted,
and fractal dimension features are calculated for acoustic feature extraction.
Furthermore, it is able to efficiently learn intrinsic characteristics of
auditory emotions, while the utterance features are learned from fractal
dimensions of each sub-bands. Experimental results show the proposed method is
able to provide comparative performance for audio emotion recognition.
