This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, the inter-annotator agreement is 64 which is interpreted as "substantial agreement". We also present benchmark results based on common classification methods in speech emotion detection task. According to the experiments, support vector machine achieves the best results for both gender-independent (58.2 ) and gender-dependent models (female=59.4 , male=57.6 ). The ShEMO is available in this http URL for academic purposes free of charge to provide a baseline for further research on Persian emotional speech.
Emotion recognition grows to an important factor in future media retrieval and man machine interfaces. However, even human deciders often experience problems realizing one's emotion, especially of strangers. In this work we strive to recognize emotion independent of the person concentrating on the speech channel. Single feature relevance of acoustic features is a critical point, which we address by filter-based gain ratio calculation starting at a basis of 276 features. As optimization of a minimum set as a whole in general saves more extraction effort, we furthermore apply an SVM-SFFS wrapper based search. For a more robust estimation we also integrate spoken content information by a Bayesian net analysis of ASR outputs. Overall classification is realized in an early feature fusion by stacked ensembles of diverse base classifiers. Tests ran on a 3,947 movie and automotive interaction dialog-turns database consisting of 35 speakers. Remarkable overall performance can be reported in the discrimination of the seven discrete emotions named in the MPEG-4 standard with added neutrality In this paper new results in instantaneous recognition of emotion in non-verbal speech are presented. As classification method dynamic programming with dynamic time warp or Bakis-hidden-Markov-models with vector quantization or Gaussian mixtures are used to analyze the pitch and energy contour of a speech signal. As emotional states joy, anger, fear, sadness, disgust, irritation, and an additional neutral user state have been evaluated. As rather unusual innovative user states the influences of tiredness and alcohol consumption of a speaker on his speech have been analyzed by use of the same methods. One of the main goals of the presented work is to keep the models applicable for new users and to keep recognition simple for real-time evaluation. Finally the observed results are presented and discussed. We have recorded a new corpus of emotionally coloured conversations. Users were recorded while holding conversations with an operator who adopts in sequence four roles designed to evoke emotional reactions. The operator and the user are seated in separate rooms; they see each other through teleprompter screens, and hear each other through speakers. To allow high quality recording, they are recorded by five high-resolution, high framerate cameras, and by four microphones. All sensor information is recorded synchronously, with an accuracy of 25 μs. In total, we have recorded 20 participants, for a total of 100 character conversational and 50 non-conversational recordings of approximately 5 minutes each. All recorded conversations have been fully transcribed and annotated for five affective dimensions and partially annotated for 27 other dimensions. The corpus has been made available to the scientific community through a web-accessible database.