The Automatic Recognition of Sepedi Speech Emotions Based on Machine Learning Algorithms

Manamela, Phuti John; Manamela, Madimetja Jonas; Modipa, Thipe I.; Sefara, Tshephisho Joseph; Mokgonyane, Tumisho Billson

doi:10.1109/icabcd.2018.8465403

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…Multiple classifiers could use the information provided by the feature extraction technique to determine the portrayed emotion, such as k nearest neighbors (Ali et al , 2019), support vector machine (Hammami et al , 2020), multi-layer perceptron (MLP) (Manamela et al , 2018), random forest (Nugroho et al , 2019) and Naive Bayes (Syed et al , 2021). All of the abovementioned classifiers would perform adequately, and each classifier would present a confusion matrix that could be aggregated; as such, the MLP was selected to demonstrate the approach.…”

Section: Research Contextmentioning

confidence: 99%

Utility indicator for emotion detection in a speaker authentication system

Rensburg

Botha

Solms

2022

ICS

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Purpose Authenticating an individual through voice can prove convenient as nothing needs to be stored and cannot easily be stolen. However, if an individual is authenticating under duress, the coerced attempt must be acknowledged and appropriate warnings issued. Furthermore, as duress may entail multiple combinations of emotions, the current f-score evaluation does not accommodate that multiple selected samples possess similar levels of importance. Thus, this study aims to demonstrate an approach to identifying duress within a voice-based authentication system. Design/methodology/approach Measuring the value that a classifier presents is often done using an f-score. However, the f-score does not effectively portray the proposed value when multiple classes could be grouped as one. The f-score also does not provide any information when numerous classes are often incorrectly identified as the other. Therefore, the proposed approach uses the confusion matrix, aggregates the select classes into another matrix and calculates a more precise representation of the selected classifier’s value. The utility of the proposed approach is demonstrated through multiple tests and is conducted as follows. The initial tests’ value is presented by an f-score, which does not value the individual emotions. The lack of value is then remedied with further tests, which include a confusion matrix. Final tests are then conducted that aggregate selected emotions within the confusion matrix to present a more precise utility value. Findings Two tests within the set of experiments achieved an f-score difference of 1%, indicating, Mel frequency cepstral coefficient, emotion detection, confusion matrix, multi-layer perceptron, Ryerson audio-visual database of emotional speech and song (RAVDESS), voice authentication that the two tests provided similar value. The confusion matrix used to calculate the f-score indicated that some emotions are often confused, which could all be considered closely related. Although the f-score can represent an accuracy value, these tests’ value is not accurately portrayed when not considering often confused emotions. Deciding which approach to take based on the f-score did not prove beneficial as it did not address the confused emotions. When aggregating the confusion matrix of these two tests based on selected emotions, the newly calculated utility value demonstrated a difference of 4%, indicating that the two tests may not provide a similar value as previously indicated. Research limitations/implications This approach’s performance is dependent on the data presented to it. If the classifier is presented with incomplete or degraded data, the results obtained from the classifier will reflect that. Additionally, the grouping of emotions is not based on psychological evidence, and this was purely done to demonstrate the implementation of an aggregated confusion matrix. Originality/value The f-score offers a value that represents the classifiers’ ability to classify a class correctly. This paper demonstrates that aggregating a confusion matrix could provide more value than a single f-score in the context of classifying an emotion that could consist of a combination of emotions. This approach can similarly be applied to different combinations of classifiers for the desired effect of extracting a more accurate performance value that a selected classifier presents.

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Section: Research Contextmentioning

confidence: 99%

Utility indicator for emotion detection in a speaker authentication system

Rensburg

Botha

Solms

2022

ICS

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“…Manamela P. J., Manamela M. J., Modipa T. I., Sefara T. J. and Mokgonyane T. B. et. al., [15] explains that machine learning algorithms are used to automatically recognize the emotions in Sepedi speech. In this research, a Speech emotion recognition (SER) system that can recognize and classify six basic emotions from speech in South Africa's official language, Sepedi, is examined: anger, sadness, disgust, fear, happiness, and neutral.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Speech Recognition System Using Fuzzy Neural Network

Babu,

Mutukuru

2020

TURCOMAT

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An important field in digital speech processing is human voice. Speech recognition in humans has long been a hot topic in signal processing and artificial intelligence research. Natural Language Processing (NLP) offers an interdisciplinary subfield called speech recognition that makes it easier for machines to recognize spoken language and transform it into text. To communicate with machines, speech recognition machines has the potential to be helpful. Technology that can have communicates in real time has been made possible. However, there remain additional issues, such as speaker variance due to other factors like age, gender, signal speed, pronunciation variations, noise from the surrounding area etc. Classification of age and gender is important for speech processing. A lot of work has been done to enhance each of these phases in order to get better and more accurate results. The main goal of this analysis is on the integration of machine learning into the speech recognition system. Hence, this analysis presents a novel speech recognition system using Fuzzy neural network. Pre-processing, speech signal segmentation, speech feature extraction, and speaker recognition are the different phases of a speech recognition system. It uses a fuzzy neural network to identify the speaker's age and gender. Accuracy, F1-score, and Precision are used to evaluate the fuzzy model's performance.

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“…B. Singh and S. Goel, 2018), discusses about the popular databases, and machine learning models to be applicable for the system, the machine leaning models specifies by them are typically listed in the table 1. The research problem of the SSECP is gaining popularity of various language across the globe, The work by (P. J. Manamela, M. J. Manamela, T. I. Modipa, T. J. Sefara and T. B. Mokgonyane, 2018) uses database of one of the African language and uses a tool for the feature extraction and the analysis taken place using a datamining tool WEKA and validate its performance with KNN and SVM [7]. Few very archival journals in this domain are studies and the inference of those papers is listed in the table 2.…”

Section: Review Of Literaturementioning

confidence: 99%

Classifying Emotional traits from Speech file using Machine Learning

Nayak S*,

Hiremath,

Birader

2019

IJITEE

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With the proliferated usage of speech as a voice command over various devices in existing times, it offers hands-free as well as comfortable experience for operating the current trends of devices. However, the devices are operated just on the basis of recognition of the speech commands and not its context. It is now well known that context factor plays a contributory role in mechanism an artificial intelligence in such devices where a comprehensive analytical modeling can be carried out. In the direction of extraction contextual information from speech, it is first necessary to recognize the speech in it logical form which can be disrupted because of various external causes. Apart from this, even if the recognition is carried out well than the next challenging part will be to extract the emotional factor within the speech. The existing review shows that research-based approaches are not sufficient enough to extract meaning full information which causes a greater degree of impediment in performing classification. Therefore, the proposed system introduces a novel and cost effective framework where machine learning is utilized in a very unique manner for performing this task. The paper discusses about an analytica approach which perform feature extraction followed by applying machine learning to show that proposed system offers faster response time with higher matching found during the testing operation phase.

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The Automatic Recognition of Sepedi Speech Emotions Based on Machine Learning Algorithms

Cited by 13 publications

References 8 publications

Utility indicator for emotion detection in a speaker authentication system

Utility indicator for emotion detection in a speaker authentication system

A Novel Speech Recognition System Using Fuzzy Neural Network

Classifying Emotional traits from Speech file using Machine Learning

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