In this paper, research on the automatic recognition of basic emotions in spoken Finnish is reported. The investigation was carried out utilizing the MediaTeam Emotional Speech corpus, which is currently the largest emotional speech database for Finnish. In this investigation, three experiments were carried out. In the first two experiments, mainly speaker-dependent automatic classification of ...

Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people talking tone like happiness, anger, and sadness. Such emotions are directly affected by the patient health status. In neutral talking environments, speakers can be easily verified, however, in emotional talking environments...

This paper reports on a recent development in Japan, funded by the Science and Technology Agency, for the creation and analysis of a very large database, including emotional speech, for the purpose of speech technology research. In order to capture spontaneous samples of naturally-occurring emotion without recourse to acting, a very large amount of conversational speech and daily spoken interac...

Setup of an emotion recognition or emotional speech recognition system is directly related to how emotion changes the speech features. In this research, the influence of emotion on the anger and happiness was evaluated and the results were compared with the neutral speech. So the pitch frequency and the first three formant frequencies were used. The experimental results showed that there are lo...

This work is aimed at exploiting Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) as classifiers to enhance talking condition recognition in stressful and emotional talking environments (completely two separate environments). The stressful talking environment that has been used in this work uses Speech Under Simulated and Actual Stress (SUSAS) database, while the emotional t...

A new diphone database with a full diphone set for each of three levels of vocal effort is presented. A theoretical motivation is given why this kind of database will be useful for emotional speech synthesis. Two hypotheses are verified in perception experiments: (I) The three diphone sets are perceived as belonging to the same speaker; (II) The vocal effort intended during database recordings ...

The man-machine relation has demanded the smart trends that machines have to react after considering the human emotional levels. The technology boost improved the machine intelligence that it gained the capability to identify human emotions at expected level. Harnessing the approaches of signal processing and pattern recognition algorithms a smart and emotions specific man-machine interaction c...

In early research the basic acoustic features were the primary choices for emotion recognition from speech. Most of the feature vectors were composed with the simple extracted pitch-related, intensity related, and duration related attributes, such as maximum, minimum, median, range and variability values. However, researchers are still debating what features influence the recognition of emotion...

This study investigates the relationship between emotional states and prosody. A prosody detection algorithm was applied to emotional speech to extract accents and intonational boundaries automatically and these were compared with hand-labeled prosodic units. The measurements used in the detection algorithm are derived from duration, pitch, harmonic structure, spectral tilt, and amplitude. The ...

Analysis of speech for recognition of stress is important for identification of emotional state of person. This can be done using ‘Linear Techniques’, which has different parameters like pitch, vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of features extraction. Anal...