When a person is in a distress situation, there are signs which are reflected in his speech or in the audio of its surroundings.
The project deals with speaker-independent distress detection in speech of a single speaker.
The solution involves the extraction of relevant features from the speech signal and the comparison between the different methods of extraction.
A distress situation is defined by the discrete emotions of anger and fear.
The project concludes with the classification of distress in speech with 91% accuracy, using the Berlin Emotional Speech Database in the German language.
