  Automatic emotion recognition in speech is a research area with a wide range
of applications in human interactions. The basic mathematical tool used for
emotion recognition is Pattern recognition which involves three operations,
namely, pre-processing, feature extraction and classification. This paper
introduces a procedure for emotion recognition using Hidden Markov Models
(HMM), which is used to divide five emotional states: anger, surprise,
happiness, sadness and neutral state. The approach is based on standard speech
recognition technology using hidden continuous markov model by selection of low
level features and the design of the recognition system. Emotional Speech
Database from Telugu Rural Dialects of Andhra Pradesh (TRDAP) was designed
using several speaker's voices comprising the emotional states. The accuracy of
recognizing five different emotions for both genders of classification is 80%
for anger-emotion which is achieved by using the best combination of
39-dimensioanl feature vector for every frame (13 MFCCs, 13 Delta Coefficients
and 13 Acceleration Coefficients) and a classifier using HMM. This outcome very
much matches with that acquired with the same database with subjective
evaluation by human judges. Both gender-dependent and gender-independent
experiments are conducted on TRDAP emotional speech database.
