  Robust speech emotion recognition relies on the quality of the speech
features. We present speech features enhancement strategy that improves speech
emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We
identified subsets from the features set and applied Principle Component
Analysis to the subsets. Finally, the features are fused horizontally. The
resulting feature set is analyzed using t-distributed neighbour embeddings
(t-SNE) before the application of features for emotion recognition. The method
is compared with the state-of-the-art methods used in the literature. The
empirical evidence is drawn using two well-known datasets: Emotional Speech
Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song
(RAVDESS) for two languages, German and English, respectively. Our method
achieved an average recognition gain of 11.5\% for six out of seven emotions
for the EMO-DB dataset, and 13.8\% for seven out of eight emotions for the
RAVDESS dataset as compared to the baseline study.
