Abstract:
Speech signals is being considered as most effective means of communication
between human beings. Many researchers have found different methods or systems
to identify emotions from speech signals. Here, the various features of speech are
used to classify emotions. Features like pitch, tone, intensity are essential for
classification. Large number of the datasets are available for speech emotion
recognition. Firstly, the extraction of features from speech emotion is carried out
and then another important part is classification of emotions based upon speech.
Hence, different classifiers are used to classify emotions such as Happy, Sad,
Anger, Surprise, Neutral, etc. Although, there are other approaches based on
machine learning algorithms for identifying emotions.
Speech Emotion Recognition is a current research topic because of its wide range of
applications and it became a challenge in the field of speech processing too. We
have carried out a brief study on Speech Emotion Analysis along with Emotion
Recognition. Speech Emotion Recognition (SER) can be defined as extraction of
the emotional state of the speaker from his or her speech signal. There are few
universal emotions including Neutral, Anger,. we have worked on different tools to
be used in SER. SER is tough because emotions are subjective and annotating
audio is challenging task.
Emotion recognition is the part of speech recognition which is gaining more
popularity and need for it increases enormously. We have classified based on
different type of emotions to detect from speech.
