  Recently, increasing attention has been directed to the study of the speech
emotion recognition, in which global acoustic features of an utterance are
mostly used to eliminate the content differences. However, the expression of
speech emotion is a dynamic process, which is reflected through dynamic
durations, energies, and some other prosodic information when one speaks. In
this paper, a novel local dynamic pitch probability distribution feature, which
is obtained by drawing the histogram, is proposed to improve the accuracy of
speech emotion recognition. Compared with most of the previous works using
global features, the proposed method takes advantage of the local dynamic
information conveyed by the emotional speech. Several experiments on Berlin
Database of Emotional Speech are conducted to verify the effectiveness of the
proposed method. The experimental results demonstrate that the local dynamic
information obtained with the proposed method is more effective for speech
emotion recognition than the traditional global features.
