Speech Emotion Recognition: Models Implementation &amp; Evaluation

Dhaouadi, Sabrine; Abdelkrim, Hedi; Saoud, Slim Ben

doi:10.1109/aset.2019.8871014

Cited by 4 publications

(2 citation statements)

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“…It can be applicable in Twitter textual tweets classification, women's e-commerce clothing reviews, and to identify the reviews of the products. Wang et al (2021) [10] a deep learning model was put up to extract emotional connections from material written in natural language. Previous artificial intelligence research concentrated on detecting sentiment rather than investigating why sentiments are not or are identified erroneously.…”

Section: General Architecture Of Speech Emotion Detectionmentioning

confidence: 99%

A Review on Recognizing of Positive or Negative Emotion Based on Speech

Kumar¹,

Rani²,

Sai³

et al. 2023

IJRASET

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Emotions are the feelings of a person and the reaction to a situation. People can verbally or nonverbally convey their feelings. In the literature ofSpeech emotion recognition, many of the techniques have used traditional ways to detect emotions, this paper present different way of recognizing emotions, by speech signals are processed by CNN to extract features, the extracted features are then used to input SVM. The SVM outputs the predicted emotions. From this approach we improved accuracy by testing and training the models based on the input audio data

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Section: General Architecture Of Speech Emotion Detectionmentioning

confidence: 99%

A Review on Recognizing of Positive or Negative Emotion Based on Speech

Kumar¹,

Rani²,

Sai³

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…Upon testing, the results demonstrated an F1 accuracy score of approximately 68%, surpassing the accuracy of previous models for the MELD data. Sabrine Dhaouadi et al [4] the classification of opposing emotions was achieved through the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). Human speech can be analyzed to extract various temporal and spectral characteristics.…”

Section: Literature Surveymentioning

confidence: 99%

Integrated Approach for Emotion Detection via Speech and Text Analysis

Birari,

Walunjkar,

Dandavate

et al. 2023

IJRITCC

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This paper aims to provide a comprehensive solution for effective reviews using deep learning models. Customers often have difficulty to find accurate reviews of the things they are interested in. The proposed framework implements a review mechanism to address this problem, which will give customers relevant reviews based on video reviews supplied in the product description. The goal of this system is to turn video reviews into a particular rating so that viewers may get a summary of the review without having to watch the full thing by simply glancing at the rating. Deep learning neural networks are used by the model for both text and audio processing in order to achieve this. The well-known RAVDESS dataset serves as the basis for the audio model's training and offers a wide range of emotional expressions. The suggested system uses two methods to provide reviews: text-based natural language processing and audio frequency spectrograms. By utilizing these two techniques, it may provide consumers accurate and trustworthy ratings while guaranteeing that the review procedure is not impeded. The aim is achieved with high accuracy to ensure that users can make informed decisions when purchasing products based on the provided reviews. With the aid of this review system, customers will be able to quickly find out crucial details about a product they are interested in, thus increasing their pleasure and loyalty.

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