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 h and 25 min 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 will be available for academic purposes free of charge to provide a baseline for further research on Persian emotional speech.
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Upon publishing this paper, we release our database for academic purposes.
The prompt excludes any emotional contents in order not to intervene the expression and perception of emotional states.
Cohen’s kappa ranges generally from 0 to 1, where large numbers indicate higher reliability and values near zero suggest that agreement is attributable to chance alone.
As Landis and Koch (1977) explain, \(0.61< kappa < 0.80\) is interpreted as “substantial agreement” among the judges.
The IPA was devised by the International Phonetic Association as a standardized representation of the sounds of oral language.
It contains 88 different parameters. For further information, please refer to Eyben et al. (2016).
Happiness has the lowest number of utterances after fear. As mentioned before, fear utterances were ignored in the classification experiments.
Actors were asked to read 10 short emotionally neutral sentences.
We trained the models on the audio (not video), speech (not song) files of the database.
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We would like to thank the anonymous reviewers for their insightful comments and suggestions. We also gratefully thank Dr. Steve Cassidy for his helpful points.
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Mohamad Nezami, O., Jamshid Lou, P. & Karami, M. ShEMO: a large-scale validated database for Persian speech emotion detection. Lang Resources & Evaluation 53, 1–16 (2019). https://doi.org/10.1007/s10579-018-9427-x
- Emotional speech
- Speech database
- Emotion detection