Study of prosodic feature extraction for multidialectal Odia speech emotion recognition

Swain, Monorama; Routray, Aurobinda; Kabisatpathy, Prithviraj; Kundu, Jogendra Nath

doi:10.1109/tencon.2016.7848296

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…For Indian languages, [20] has shown work for Assamese using Gaussian Mixture Model, with performance of 74% and highest mean classification score as 76.5 and [21] has shown work for Odia language using prosodic features and Support Vector Machine, with 75% performance. Tamil has been explored by [22] with 71.3%, Bengali by [23] with 74.26%, Malayalam by [24] with Support Vector Machine and Artificial Neural Network with 88.4% performance, Telugu by [22], [25] with performance of 81% and Hindi with 74% [26].…”

Section: Speech Emotion Recognition Processmentioning

confidence: 99%

Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Kaur,

Singh

2023

AJSE

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Speech Emotion Recognition (SER) plays a predominant role in human-machine interaction. SER is a challenging task because of number of complexities involved in it. For an accurate emotion classification system, feature extraction is the first and important step carried out on speech signals. And after the features are extracted, it is very important to select the best features out of all and reject the redundant and least important features. Feature selection methods play an important role in SER performance. The classifier gets the selected features, so as to reduce the unnecessary overload and perform better to classify the emotions. In this study, a good combination of features is selected from Punjabi Emotional Speech Database. Then a number of feature selection algorithms are explored and experimented upon, to select the best features. 1D-CNN is used for classification purpose. The results are shown and compared on the basis of number of performance metrics. LASSO has shown the best performance results as compared to other feature selection methods.

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Section: Speech Emotion Recognition Processmentioning

confidence: 99%

Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Kaur,

Singh

2023

AJSE

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“…Among Indian languages, the work on Assamese and Marathi languages has been done using MFCC and Gaussian Mixture Model classifier, with highest mean classification score of 74% [24]. However, [25] have shown that Support Vector Models give better results than Gaussian Mixture Model with highest accuracy of 75%, for the Sambalpuri, Cuttacki, and Berhampuri dialects of Odia language based on various prosodic features. The Support Vector Machine classifier has also been used for Tamil with result of 71.3% and Bengali language with 74.26% [26], [27].…”

Section: Related Workmentioning

confidence: 99%

Impact of Feature Extraction and Feature Selection Algorithms on Punjabi Speech Emotion Recognition Using Convolutional Neural Network

Kaur

Singh

2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The challenge to refine the spontaneity and productivity of a machine and human coherence, speech emotion recognition has been an overriding area of research. The trustability and fulfillment of such emotion recognition are largely involved with the feature extraction and selection processes. An important role is played in exploring and distinguishing audio content during the feature extraction phase. Also, the features that have been extracted should be tough to a number of disturbances and reliable enough for an adequate classification system. This paper focuses on three main components of a Speech Emotion Recognition (SER) Process. The first one is the optimal feature extraction method for Punjabi SER system. The second one is the use of an appropriate feature selection method that desires to select effectual features from the ones extracted in the first step, and removes the redundant features, to improve the conduct of emotion recognition. The third one is the classification model that has been used further for emotion recognition. So, the scope of this paper is to explain the three main steps of Punjabi SER system, feature extraction, feature selection, and emotion recognition with classifier. The results have been calculated and compared for number of feature set combinations, with and without feature selection process. A total of 10 experiments are carried out and various performance metrics such as precision, recall, F1-score, accuracy, etc. are used to demonstrate the results.

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“…The relevance of fixed effects on mean intensity was established by comparing null effect models with fixed effect = 1 to two models, one with emotion as the fixed effect factor and the other with emotion and gender as fixed effect factors as shown in equation ( 8), ( 9) and (10).…”

Section: ) Modeling Mean Intensitymentioning

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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Study of prosodic feature extraction for multidialectal Odia speech emotion recognition

Cited by 10 publications

References 11 publications

Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Impact of Feature Extraction and Feature Selection Algorithms on Punjabi Speech Emotion Recognition Using Convolutional Neural Network

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

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