  This research aims at identifying the unknown emotion using speaker cues. In
this study, we identify the unknown emotion using a two-stage framework. The
first stage focuses on identifying the speaker who uttered the unknown emotion,
while the next stage focuses on identifying the unknown emotion uttered by the
recognized speaker in the prior stage. This proposed framework has been
evaluated on an Arabic Emirati-accented speech database uttered by fifteen
speakers per gender. Mel-Frequency Cepstral Coefficients (MFCCs) have been used
as the extracted features and Hidden Markov Model (HMM) has been utilized as
the classifier in this work. Our findings demonstrate that emotion recognition
accuracy based on the two-stage framework is greater than that based on the
one-stage approach and the state-of-the-art classifiers and models such as
Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Vector
Quantization (VQ). The average emotion recognition accuracy based on the
two-stage approach is 67.5%, while the accuracy reaches to 61.4%, 63.3%, 64.5%,
and 61.5%, based on the one-stage approach, GMM, SVM, and VQ, respectively. The
achieved results based on the two-stage framework are very close to those
attained in subjective assessment by human listeners.
