Recognition of Emotions from Speech using Excitation Source Features

Koolagudi, Shashidhar G.; Devliyal, Swati; Chawla, Bhavna; Barthwal, Anurag; Rao, K. Sreenivasa

doi:10.1016/j.proeng.2012.06.394

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…A semi-natural database GEU-SNESC (GEU Semi Natural Emotion Speech Corpus), was proposed [49]. Five emotions: happy, sad, anger, surprise, and neutral, were considered for the classification using the GMM classifier.…”

Section: ) Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and correlate emotional aspects of speech signals are quite contrasting quantitatively and qualitatively, which present enormous difficulties in blending knowledge from interdisciplinary fields, particularly speech emotion recognition, applied psychology, and human-computer interface. The paper carefully identifies and synthesizes recent relevant literature related to the SER systems' varied design components/methodologies, thereby providing readers with a state-of-the-art understanding of the hot research topic. Furthermore, while scrutinizing the current state of understanding on SER systems, the research gap's prominence has been sketched out for consideration and analysis by other related researchers, institutions, and regulatory bodies.

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Section: ) Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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“…Features dictate the performance of a Speech Emotion Recognition system, and identifying emotional states require various features [7]. For the expression of emotions, speech features contribute in particular ways [29]. But the most salient features can improve the performance of a model exponentially.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Support Vector Machine (SVM) [27] is one of the conventional classifiers deployed in Speech Emotion Recognition Systems [3], [6], [15], [34], [41]- [45]. The models built by an SVM classifier designate new training examples to any one category.…”

Section: Classifiermentioning

confidence: 99%

A Case Study on the Independence of Speech Emotion Recognition in Bangla and English Languages using Language-Independent Prosodic Features

Saad¹,

Mahmud²,

Kabir³

et al. 2021

Preprint

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A language agnostic approach to recognizing emotions from speech remains an incomplete and challenging task. In this paper, we used Bangla and English languages to assess whether distinguishing emotions from speech is independent of language. The following emotions were categorized for this study: happiness, anger, neutral, sadness, disgust, and fear. We employed three Emotional Speech Sets, of which the first two were developed by native Bengali speakers in Bangla and English languages separately. The third was the Toronto Emotional Speech Set (TESS), which was developed by native English speakers from Canada. We carefully selected language-independent prosodic features, adopted a Support Vector Machine (SVM) model, and conducted three experiments to carry out our proposition. In the first experiment, we measured the performance of the three speech sets individually. This was followed by the second experiment, where we recorded the classification rate by combining the speech sets. Finally, in the third experiment we measured the recognition rate by training and testing the model with different speech sets. Although this study reveals that Speech Emotion Recognition (SER) is mostly language-independent, there is some disparity while recognizing emotional states like disgust and fear in these two languages. Moreover, our investigations inferred that non-native speakers convey emotions through speech, much like expressing themselves in their native tongue.

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“…Suitable feature selection is an important task because it carries intended information and it decides overall efficiency of system. Generally three kinds of features are extracted from database 1) Excitation Source features like LP residual, glottal excitation signal, 2) Vocal track features like MFCC, LPCC 3) prosodic features like pitch, formants 4) Hybrid features [14,15].…”

Section: Basic Frmework Of Speech Emotion Recognition (Ser)mentioning

confidence: 99%

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

Tank

Hadia

2020

IJECE

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In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

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Recognition of Emotions from Speech using Excitation Source Features

Cited by 13 publications

References 9 publications

A Comprehensive Review of Speech Emotion Recognition Systems

A Comprehensive Review of Speech Emotion Recognition Systems

A Case Study on the Independence of Speech Emotion Recognition in Bangla and English Languages using Language-Independent Prosodic Features

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

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