A Study of the Effect of Emotions and Software on Prosodic Features on Spoken Utterances in Urdu Language

Ali, Syed Abbas; Andleeb, Maria; Rehman, Danish

doi:10.5815/ijigsp.2016.04.06

Cited by 2 publications

(1 citation statement)

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“…Researchers often analyzed the database statistically after acoustic analysis to validate the acoustic analysis results and then select the best prosodic features to construct a prosody model [14][15][16][17][18][19]. Analysis of variance (ANOVA) findings investigate statistical discriminations of prosodic properties between various emotion classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Linear Mixed Effect Modelling for Analyzing Prosodic Parameters for Marathi Language Emotions

Harhare¹,

Shah²

2021

IJACSA

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Along with linguistic messages, prosody is an essential paralinguistic component of emotional speech. Prosodic parameters such as intensity, fundamental frequency (F0), and duration were studied worldwide to understand the relationship between emotions and corresponding prosody features for various languages. For evaluating prosodic aspects of emotional Marathi speech, the Marathi language has received less attention. This study aims to see how different emotions affect suprasegmental properties such as pitch, duration, and intensity in Marathi's emotional speech. This study investigates the changes in prosodic features based on emotions, gender, speakers, utterances, and other aspects using a database with 440 utterances in happiness, fear, anger, and neutral emotions recorded by eleven Marathi professional artists in a recording studio. The acoustic analysis of the prosodic features was employed using PRAAT, a speech analysis framework. A statistical study using a two-way Analysis of Variance (two-way ANOVA) explores emotion, gender, and their interaction for mean pitch, mean intensity, and sentence utterance time. In addition, three distinct linear mixed-effect models (LMM), one for each prosody characteristic designed comprising emotion and gender factors as fixed effect variables, whereas speakers and sentences as random effect variables. The relevance of the fixed effect and random effect on each prosodic variable was verified using likelihood ratio tests that assess the goodness of fit. Based on Marathi's emotional speech, the R programming language examined linear mixed modeling for mean pitch, mean intensity, and sentence duration.

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