MDER-MA: Multimodal Emotion Recognition Dataset for the Moroccan Arabic

MDER-MA, a Multimodal Emotion Recognition Dataset for Moroccan Arabic, contain 5288 data items that express one of the four emotions: Happy, Sad, Angry, and Neutral, expressed in four different modalities: audio, text, spectrogram, and Mel-spectrogram images. Each modality contains 1,322 samples. The samples were collected from various regions across Morocco to ensure the creation of a representative dataset that is not biased toward any single geographic or linguistic area. MDER-MA supports multiple applications, including emotion recognition, audio transcription, age and gender identification from both speech, text, and image modalities. Annotation was conducted by five native Moroccan speakers, ensuring high linguistic reliability for real-time emotion recognition tasks. This work aims to bridge the gap between high-resource and low-resource languages in the field of emotion-aware and humanized intelligent systems, and to foster the development of Arabic language technologies, with particular focus on regional dialects such as Moroccan Arabic.