An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

Kumar, S. Adish; Jason, C. Andy

doi:10.35940/ijrte.e5715.018520

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…where 𝑃 (𝐻 𝑖 | 𝐷) is the posterior probability, 𝑃 (𝐷 |𝐻 𝑖 ) is the likelihood, 𝑃 (𝐻 𝑖 ) is the class prior probability, and 𝑃 (𝐷) is the detector prior probability [29].…”

Section: 𝑔(𝑦) = 𝛽𝑜 + 𝛽 (𝑁𝑢𝑚) (Iii)mentioning

confidence: 99%

WITHDRAWN: Fake News Detection Using Ensemble Techniques

Malhotra

Malik

2023

Preprint

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Fake news is misinformation or distorted news that is spread over social media with the intent of harming an individual, organization, or government body. Collecting news from an internet platform is simple because it is quick, user-friendly, and constantly updated. However, this information is also subject to personal preferences or interests, which can negatively affect individuals or organizations. As a result, it is critical to detect the spread of fake news by computational methods. Semantic web approaches also plays an important role in detecting false news since they are used to understand the data that is available online in the same manner that humans do. As a result, this research examined various learning models for classifying whether the information is true or false using a fake and real news dataset. Around 40,000 items were analyzed, including approximately 20,000 from each fake and actual news dataset. Ensemble learning models such as support vector machine, logistic regression, CatBoost, XgBoost, multinomial, Naive Bayes, and random forest were used and evaluated using recall, accuracy, false rejection rate (FRR), F1 score, precision, negative predictive value (NPV), false discovery rate (FDR), and Matthews' correlation coefficient. Based on these evaluations, the best learning models were hybridized and computed alongside the passive-aggressive classifier and deep Auto_ViML model. Following computation, it was determined that the deep Auto_ViML model had the highest accuracy, precision, recall, and F1 score of 99%. In contrast, the hybrid learning model had the best value of false rejection rate of 71%. In comparison, the support vector machine was computed in 0.000245 seconds.

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Section: 𝑔(𝑦) = 𝛽𝑜 + 𝛽 (𝑁𝑢𝑚) (Iii)mentioning

confidence: 99%

WITHDRAWN: Fake News Detection Using Ensemble Techniques

Malhotra

Malik

2023

Preprint

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“…Subsequently, in the process, a gray filter is applied to eliminate the different color channels to later detect some important parts of the face such as the nose, eyes, eyebrows, and mouth [39], [40]. By applying a facial point technique, it is possible to detect an initial reference point in the center of the nose and to identify various facial points on parts of the face [41].…”

Section: Discussionmentioning

confidence: 99%

Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques

Villegas-Ch¹,

García-Ortiz²,

Sánchez-Viteri

2023

IEEE Access

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Educational models currently integrate a variety of technologies and computer applications that seek to improve learning environments. With this objective, information technologies have increasingly adapted to assume the role of educational assistants that support the teacher, the students, and the areas enrolled in educational quality. One of the technologies that are gaining strength in the academic field is computer vision, which is used to monitor and identify the state of mind of students during the teaching of a subject. To do this, machine learning algorithms monitor student gestures and classify them to identify the emotions they convey in a teaching environment. These systems allow the evaluation of emotional aspects, based on two main elements, the first is the generation of an image database with the emotions generated in a learning environment such as interest, commitment, boredom, concentration, relaxation, and enthusiasm. The second is an emotion recognition system, through the recognition of facial gestures using non-invasive techniques. This work applies techniques for the recognition and processing of facial gestures and the classification of emotions focused on learning. This system helps the tutor in a modality of face-toface education and allows him to evaluate emotional aspects and not only cognitive ones. This arises from the need to create a base of images focused on the spontaneous learning of emotions since most of the works reviewed focus on these acted-out emotions.INDEX TERMS Computer vision, emotion recognition, neural networks, teaching.

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“…At present, many domestic and foreign researchers are devoted to the research of multi-modal fusion emotion recognition. But most of them use traditional feature extraction methods, such as Hidden Markov Model (HMM) and Gaussian mixed Model (GMM) models (Andy and Kumar, 2020). aimed at the detection and classification of microcalcification clusters by computer-aided diagnosis systems, using an adaptive GMM model to extract spatial distribution statistical features, which has high accuracy.…”

Section: Related Researchmentioning

confidence: 99%

Multi-Modal Fusion Emotion Recognition Method of Speech Expression Based on Deep Learning

Liu

Wang

et al. 2021

Front. Neurorobot.

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The redundant information, noise data generated in the process of single-modal feature extraction, and traditional learning algorithms are difficult to obtain ideal recognition performance. A multi-modal fusion emotion recognition method for speech expressions based on deep learning is proposed. Firstly, the corresponding feature extraction methods are set up for different single modalities. Among them, the voice uses the convolutional neural network-long and short term memory (CNN-LSTM) network, and the facial expression in the video uses the Inception-Res Net-v2 network to extract the feature data. Then, long and short term memory (LSTM) is used to capture the correlation between different modalities and within the modalities. After the feature selection process of the chi-square test, the single modalities are spliced to obtain a unified fusion feature. Finally, the fusion data features output by LSTM are used as the input of the classifier LIBSVM to realize the final emotion recognition. The experimental results show that the recognition accuracy of the proposed method on the MOSI and MELD datasets are 87.56 and 90.06%, respectively, which are better than other comparison methods. It has laid a certain theoretical foundation for the application of multimodal fusion in emotion recognition.

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An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

Cited by 9 publications

References 53 publications

WITHDRAWN: Fake News Detection Using Ensemble Techniques

WITHDRAWN: Fake News Detection Using Ensemble Techniques

Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques

Multi-Modal Fusion Emotion Recognition Method of Speech Expression Based on Deep Learning

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