Persian speech emotion recognition

Savargiv, Mohammad; Bastanfard, Azam

doi:10.1109/ikt.2015.7288756

Cited by 23 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future developments could combine the proposed approach with approaches that use Mel coefficients to improve the efficiency of various SED tasks. In addition, IMF properties can be used in other speech languages such as sound persian phoneme articulation [90,91] or speech synthesis [92]. The analysis of IMF applicability in SED systems, considering varying levels of signal-to-noise ratios, is another suggested topic for future investigation.…”

Section: Discussionmentioning

confidence: 99%

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Hajihashemi,

Alavigharahbagh,

Machado

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

The detection of sound events has become increasingly important due to the development of signal processing methods, social media, and the need for automatic labeling methods in applications such as smart cities, navigation, and security systems. For example, in such applications, it is often important to detect sound events at different levels, such as the presence or absence of an event in the segment, or to specify the beginning and end of the sound event and its duration. This study proposes a method to reduce the feature dimensions of a Sound Event Detection (SED) system while maintaining the system’s efficiency. The proposed method, using Empirical Mode Decomposition (EMD), Intrinsic Mode Functions (IMFs), and extraction of locally regulated features from different IMFs of the signal, shows a promising performance relative to the conventional features of SED systems. In addition, the feature dimensions of the proposed method are much smaller than those of conventional methods. To prove the effectiveness of the proposed features in SED tasks, two segment-based approaches for event detection and sound activity detection were implemented using the suggested features, and their effectiveness was confirmed. Simulation results on the URBAN SED dataset showed that the proposed approach reduces the number of input features by more than 99% compared with state-of-the-art methods while maintaining accuracy. According to the obtained results, the proposed method is quite promising.

show abstract

Section: Discussionmentioning

confidence: 99%

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Hajihashemi,

Alavigharahbagh,

Machado

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…SER has been an emerging field since few years. In literature, various foreign languages are explored by researchers, including Chinese SER with Deep Belief Network [11], Mandarin [12], Persian [13] using Hidden Markov Model with 79.50% performance accuracy, for Polish [14] using k-nearest neighbor, Linear Discriminant Analysis, with performance of 80%. Emotion recognition has been improved by combining feature selection approaches, ranking models, and Fourier parameter models, as well as validating the models against standardized existing speech datasets including CASIA, EMODB, EESDB, FAU Aibo and LDC [15]- [17].…”

Section: Speech Emotion Recognition Processmentioning

confidence: 99%

Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Kaur,

Singh

2023

AJSE

View full text Add to dashboard Cite

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.

show abstract

“…In this technique, the classifiers have been trained with known languages, but the testing has been done on unknown or never seen languages. [15] have worked on Persian speech emotion recognition, using Hidden Markov Model and have achieved an average accuracy of 79.50%. A novel method for speech emotion recognition has been proposed by [16], which is based on hidden factor analysis, in which the acoustic features are broken down into an emotion specific component and an emotion independent specific component, where later is adopted for classification with improved accuracy.…”

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.

View full text Add to dashboard Cite

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.

show abstract

Persian speech emotion recognition

Cited by 23 publications

References 40 publications

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

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

Contact Info

Product

Resources

About