Abstract:Speech emotion recognition (SER) has grown to be one of the most trending research topics in computational linguistics in the last two decades. Speech being the primary communication medium, understanding the emotional state of humans from speech and responding accordingly have made the speech emotion recognition system an essential part of the human-computer interaction (HCI) field. Although there are a few review works carried out for SER, none of them discusses the development of SER system for the Indo-Ary… Show more
“…It is analyzed that there is no prior standardized multimodal emotion dataset, which contains recordings of speech and text of people who speak native languages in the Punjabi Language. Figure 2 shows an analysis of research works done for some of the Indian languages in the last two decades 21 . Figure 2 Analysis of experiments done on Indian languages for speech emotion recognition in the last two decades.…”
Recent research has focused extensively on employing Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNN), for Speech Emotion Recognition (SER). This study addresses the burgeoning interest in leveraging DL for SER, specifically focusing on Punjabi language speakers. The paper presents a novel approach to constructing and preprocessing a labeled speech corpus using diverse social media sources. By utilizing spectrograms as the primary feature representation, the proposed algorithm effectively learns discriminative patterns for emotion recognition. The method is evaluated on a custom dataset derived from various Punjabi media sources, including films and web series. Results demonstrate that the proposed approach achieves an accuracy of 69%, surpassing traditional methods like decision trees, Naïve Bayes, and random forests, which achieved accuracies of 49%, 52%, and 61% respectively. Thus, the proposed method improves accuracy in recognizing emotions from Punjabi speech signals.
“…It is analyzed that there is no prior standardized multimodal emotion dataset, which contains recordings of speech and text of people who speak native languages in the Punjabi Language. Figure 2 shows an analysis of research works done for some of the Indian languages in the last two decades 21 . Figure 2 Analysis of experiments done on Indian languages for speech emotion recognition in the last two decades.…”
Recent research has focused extensively on employing Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNN), for Speech Emotion Recognition (SER). This study addresses the burgeoning interest in leveraging DL for SER, specifically focusing on Punjabi language speakers. The paper presents a novel approach to constructing and preprocessing a labeled speech corpus using diverse social media sources. By utilizing spectrograms as the primary feature representation, the proposed algorithm effectively learns discriminative patterns for emotion recognition. The method is evaluated on a custom dataset derived from various Punjabi media sources, including films and web series. Results demonstrate that the proposed approach achieves an accuracy of 69%, surpassing traditional methods like decision trees, Naïve Bayes, and random forests, which achieved accuracies of 49%, 52%, and 61% respectively. Thus, the proposed method improves accuracy in recognizing emotions from Punjabi speech signals.
“…For instance, speech signals may typically be obtained more quickly and affordably than many other biological signals (such as the EKG). Because of this, most researchers are drawn to speech-emotion recognition (SER) (2) . For the SER system to be successful, the following three challenges must be addressed:…”
Section: Basics Of Emotion Recognitionmentioning
confidence: 99%
“…The result of this achieved an average test accuracy rate of 90%. Some studies are carried out for the development of an automatic SER system for Indo-Aryan and Dravidian languages (2) . This paper presents a brief study of the prominent databases available for SER experiments.…”
Objectives:The present work aims to investigate the recognition of emotion from Assamese speech. Methods: This work presents a method based on the Gaussian Mixture Model (GMM) classifier and Mel-frequency cepstral coefficients (MFCC) as feature extraction technique for emotion recognition from Assamese speeches. Findings: We have conducted experiments considering different emotions: Angry, Happy, Neutral and Sad. The speech emotion recognition system database is the emotional speech samples collected manually from 20 speakers and some standard samples available on the internet. The speakers are from different districts of Assam and use different dialects of the Assamese language, such as Western (Kamrupi), Central, and Eastern. They fall under the age group of 18-26 years. The field survey consists of recordings done at Dibrugarh University and outside the campus. After the GMM training and testing process, the accuracy we obtained is 51.25%. The experiments confirmed that angry and happy emotions have high energy in the higher frequency region. In contrast, neutral and sad emotions have low energy in the higher frequency region. Novelty: This work will help predict the attitudes and actions of different speakers based on their manner of speaking. In addition, the present work will also help in other aspects of human-machine interaction in our daily life. The Assamese emotional speech database used in the work is self-collected from different dialect groups to understand the variability of emotions in dialectal perspective.
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