Emotion recognition in Arabic speech

Klaylat, Samira; Osman, Ziad; Hamandi, Lama; Zantout, Rached

doi:10.1007/s10470-018-1142-4

Cited by 40 publications

(28 citation statements)

References 77 publications

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“…They investigated 15 emotions with five that are dominants: enthusiasm, admiration, disapproval, neutral, and joy. Samira Klaylat et al [17] collected a realistic speech corpus from the Arabic TV shows (Egyptian, Gulf, Jordan, and Lebanese) where the videos are labeled by their perceived emotions: happy, angry, or surprised. Eighteen human labelers were asked to listen to the videos and label each of them as happy, angry, or surprised, and the average result is used to label each video.…”

Section: A Arabic Speech Emotion Corporamentioning

confidence: 99%

King Saud University Emotions Corpus: Construction, Analysis, Evaluation, and Comparison

et al. 2021

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Emotional speech recognition for the Arabic language is insufficiently tackled in the literature compared to other languages. In this paper, we present the work of creating and verifying the King Saud University Emotions (KSUEmotions) corpus, which was released by the Linguistic Data Consortium (LDC) in 2017 as the first public Arabic emotional speech corpus. KSUEmotions contains an emotional speech of twenty-three speakers from Saudi Arabia, Syria, and Yemen, and includes the emotions: neutral, happiness, sadness, surprise, and anger. The corpus content is verified in two different ways: a human perceptual test by nine listeners who rate emotional performance in audio files, and automatic emotion recognition. Two automatic emotion recognition systems are experimented with: Residual Neural Network and Convolutional Neural Network. This work also experiments with emotion recognition for the English language using the Emotional Prosody Speech and Transcripts Corpus (EPST). The current experimental work is conducted in three tracks: (i) monolingual, where independent experiments for Arabic and English are carried out, (ii) multilingual, where the Arabic and English corpora are merged in as mixed corpus, and (iii) cross-lingual, where models are trained using one language and tested using the other. A challenge encountered in this work is that the two corpora do not contain the same emotions. That problem is tackled by mapping the emotions to the arousal-valance space.

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Section: A Arabic Speech Emotion Corporamentioning

confidence: 99%

King Saud University Emotions Corpus: Construction, Analysis, Evaluation, and Comparison

et al. 2021

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“…In Arabic language there are a few databases in the field of SER. Natural Arabic speech database is built, and this database comprises three emotions: happiness, angry and surprise [33]. Tunisian actors were employed to build Tunisian dialect database which contains five emotions: happy, anger, fear, sadness and neutral emotions [34].…”

Section: Related Workmentioning

confidence: 99%

Recognition of emotions in the Algerian Dialect Speech

Horkous¹,

Guerti²

2021

IJCDS

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“…Moroccan emotional database (MEDB) was created from broadcasts on the YouTube channel [38]. To recognize sentiment in natural Arabic speech, database contains utterances recorded in three emotions: happiness, angry and surprise is constructed [39].…”

Section: Related Workmentioning

confidence: 99%