The cellular services, multimedia devices and call centers have vast area of application related to emotion recognition where devices can detect the human behavior (frustration and annoyance etc.) of end user and react accordingly. In the robot-human communication, the robots can be trained to communicate with human-based emotional states.
Similarly, in medical sciences, virtual assessment of the patients’ health is possible by listening to his/her voice. There are several applications of emotional understanding such as E-learning where the tutor can change the presentation style when a learner is feeling uninterested, angry, or interested.
Hence, the literature in emotion detection research is focused on the interpretation of emotions from human speech ( Dahake, Shaw & Malathi, 2016). Among all these, speech is an easy and effective form of interaction. There are many ways in which machines can recognize emotions such as face recognition, gestures, eye movements, body language, and electrocardiogram (ECG) signals ( Soleymani et al., 2016). Emotion recognition is also a vital part of automatic human behavior analysis such as assessing candidates’ suitability for a job, assessing emotional intelligence, and lie detection, etc. Removing the disgust emotion significantly improves the performance of the classifier to 76.5%.Įmotion recognition is a vital aspect towards complete human-machine interaction since effective communications of information is fundamental to human-machine interaction. It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. To evaluate the quality of speech emotions, subjective listing tests were conducted. The classification tests were performed on the emotional speech corpus collected from 20 different subjects. The Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coefficient (LPC), energy, spectral flux, spectral centroid, spectral roll-off, and zero-crossing were used as speech descriptors. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms. In this work, we develop the first emotional speech database of the Urdu language. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. Speech interfaces offer humans an informal and comfortable means to communicate with machines.
Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing.
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