Emotion Recognition Using Short Time Speech Analysis - Emotional Engineering - page 94

3 Emotion Recognition Method Based on Purely
Segment-Level Features
The proposed methodology for emotion recognition is based on purely segment-
level speech frames, and the important issues for consideration here are the
increased number of samples that raise the computational burden in terms of both
memory capacity and execution speed and the decline in the generalization ability
of the classi ! er. In this work, we address the quantitative analysis of various
analytical schemes related to segment-level emotion recognition, and we propose an
automatic approach for decreasing the number of samples in order to reduce the
computational complexity and improve the classi ! er generalization ability. The
algorithm is illustrated in Fig. 3 .
3.1 Segmentation Approach
We propose novel segmentation strategies based on analysis of short time speech
segments. The proposed approach is illustrated in Fig. 4 .
A classi ! er is trained by using the information contained in the input feature
vectors. In the real world, the ! final uncertainty will not be ideally zero after training
because of insuf ! cient input information. In addition, the classi ! er might be
“ confused ” due to ambiguities in the input information. The most likely solution is
Fig. 3 Flowchart of emotion
recognition method based on
purely segment-level features