A Review on Emotion Detection and Classification using Speech

Tripathi, Akhilesh; Singh, Upasana; Bansal, Garima; Gupta, Rishabh; Singh, Ashutosh Kumar

doi:10.2139/ssrn.3601803

Cited by 24 publications

(11 citation statements)

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“…Numerous Algorithms for Speech Emotion Recognition have been developed [5][6][7]. We compared our method to other best-in-class strategies in Table1.…”

Section: Results Discussionmentioning

confidence: 99%

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Speech Emotion Based Sentiment Recognition using Deep Neural Networks

Choudhary

Meena

Mohbey

2022

J. Phys.: Conf. Ser.

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The capacity to comprehend and communicate with others via language is one of the most valuable human abilities. We are well-trained in our experience reading awareness of different emotions since they play a vital part in communication. Contrary to popular belief, emotion recognition is a challenging task for computers or robots due to the subjective nature of human mood. This research proposes a framework for acknowledging the passionate sections of conversation, independent of the semantic content, via the recognition of discourse feelings. To categorize the emotional content of audio files, this article employs deep learning techniques such as convolutional neural networks (CNNs) and long short-term memories (LSTMs). In order to make sound information as helpful as possible for future use, models using Mel-frequency cepstral coefficients (MFCCs) were created. It was tested using RAVDESS and TESS datasets and found that the CNN had a 97.1% accuracy rate.

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“…Numerous Algorithms for Speech Emotion Recognition have been developed [5][6][7]. We compared our method to other best-in-class strategies in Table1.…”

Section: Results Discussionmentioning

confidence: 99%

“…However, this research does not address them. A few weeks ago, Anjali et al [6] published a review that summarised speech emotion recognition techniques. Speech emotion detection features and approaches from 2009 to 2018 were briefly described and reviewed as part of the inquiry.…”

Section: Literature Reviewmentioning

confidence: 99%

Speech Emotion Based Sentiment Recognition using Deep Neural Networks

Choudhary

Meena

Mohbey

2022

J. Phys.: Conf. Ser.

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“…However, the research is not addressing the accessible approaches to overcome weaknesses. Very recently, Anjali et al [51] published a review as a summary of speech emotion detection methods. A brief discussion of various features used in speech emotion recognition and review of methods used for this purpose from 2009 to 2018 has been provided in the re-search.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models

Abbaschian

Sierra-Sosa

Elmaghraby

2021

Sensors

192

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The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.

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“…Most of the data leakage, security hacks and attacks happens during the process of data transfer by taking advantage of the security discrepencies or vulnerabilities of computing, storage, and networking devices within the cloud datacentre [84][85][86][87][88][89][90][91]. Moreover, the co-residency of virtual machines of multiple users on a common physical server often leads to malicious activities like, data hampering and leakage of cloud user's confidential data [10,[92][93][94][95][96][97][98][99][100][101][102]. The malicious user or hacker launches one or multiple of virtual machines and exploits multiple network routes by achieving co-residency with the target virtual machine [29,[103][104][105][106][107][108][109][110][111][112].…”

Section: Introductionmentioning

confidence: 99%

A Review on Secure Cloud Resource Management

Chhabra¹,

Saxena²,

Rekha³

2023

Preprint

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<p>Security is always a prime concern of every cloud user. Many pioneer surveys have been done concerning security during data communication among owners, users, and stakeholders. However, there is a lack of literature surveys that address the concern of security during data processing or the workload execution within cloud data centers. In this context, we present a concise review of the existing methods that have addressed the security of workload or user data during load scheduling and execution within cloud environments. These methods have applied load-balancing strategies that helped to minimize the security attacks via co-residency or covert channels. The pandect comparative summary of these methods follows the corresponding review. The paper is concluded with limitations and future direction concerning secure load management.</p>

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A Review on Emotion Detection and Classification using Speech

Cited by 24 publications

References 0 publications

Speech Emotion Based Sentiment Recognition using Deep Neural Networks

Speech Emotion Based Sentiment Recognition using Deep Neural Networks

Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models

A Review on Secure Cloud Resource Management

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