Content-Based Arabic Speech Similarity Search and Emotion Detection

Meddeb, Mohamed; Karray, Hichem; Alimi, Adel M.

doi:10.1007/978-3-319-48308-5_51

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Further, a person's speech can be recorded much more quickly than other signals requiring additional equipment [6]. Due to its wide range of applications, including call centers, Smart T.V.s, computer games, robot interactions, criminal investigations [7][8][9], and psychological medical diagnostics [10,11], Speech Emotion Recognition (SER) has attracted a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Feature Extraction and Selection for Emotion Recognition from Speech through Machine Learning

Ramyasree¹,

Kumar²

2023

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Speech-based emotion recognition is still challenging due to its complexity despite being widely used in applications relating to emotions. In this paper, we developed a framework by considering three features: Prosodic features, Wavelet, and Spectral features. Under Prosodic, pitch and energy are considered, while under wavelet features, the approximation and detailed sub-bands ate fur scales are considered. Mel-Frequency Cepstral Coefficients (MFCC), Formants, and Long-Term Average Spectrum (LTAS) are all measured from speech signals as part of spectral features. Further, the significant features are selected based on nonlinear statistics, and dimensionality reduction is accomplished through Fisher Criterion. Spearman Rank Correlation is employed to find the nonlinear statistics under correlation analysis. For categorization, a Support Vector Machine and Decision Tree are used. The proposed method is simulated over RAVDESS, SAVEE, EMOVO, and URDU databases, and the observed recognition rates are approximately 79.66%, 88.99%, 87.68%, and 95.78%, respectively.

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Section: Introductionmentioning

confidence: 99%

Multi-Attribute Feature Extraction and Selection for Emotion Recognition from Speech through Machine Learning

Ramyasree¹,

Kumar²

2023

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“…Prosodic and spectral features were then computed to identify happiness, anger and surprise speech emotions where 95.5% accuracy was attained. In a third research study by Meddeb et al [19], prosodic along with spectral features were considered to recognize anger, fear, happiness, neutral and sadness within a Tunisian Arabic database (REGIM_TES). The REGIM_TES database was created using 12 actors (6 males, 6 females) who uttered semantically neutral sentences while simulating the five different emotions [20].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Linguistic and Prosodic Features of Bilingual Arabic–English Speakers for Speech Emotion Recognition

2020

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Speech emotion recognition (SER) research has usually focused on the analysis of the native language of speakers, most commonly, targeting European and Asian languages. In the present study, a bilingual Arabic/English speech emotion database elicited from 16 male and 16 female Egyptian participants was created in order to investigate how the linguistic and prosodic features were affected by the anger, fear, happiness and sadness emotions across Arabic and English emotional speech. The results of the linguistic analysis indicated that the participants preferred to express their emotions indirectly, mainly using religious references, and that the female participants tended to use language that was more tentative and emotionally expressive, while the male participants tended to use language that was more assertive and independent. As for the prosodic analysis, statistical t-tests showed that the prosodic features of pitch, intensity and speech rate were more indicative of anger and happiness while less relevant to fear and scarcely significant for sadness. Furthermore, speech emotion recognition performed using linear support vector machine (SVM) with AdaBoost also supported these results. In regard to first and second language linguistic features, there was no significant difference in the choice of words and structures expressing the different emotions in the two languages, but in terms of prosodic features, the females' speech showed higher pitch in Arabic in all cases while both genders showed close intensity values in the two languages and faster speech rate in Arabic than in English. INDEX TERMS Arabic speech analysis, bilingual linguistic and prosodic features, classification, speech emotion recognition. LAMIAA ABDEL-HAMID received the Ph.D. degree (Hons.

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