Automatic Speech Emotion Recognition Using Machine Learning

Kerkeni, Leila; Serrestou, Youssef; Mbarki, Mohamed; Raoof, Kosai; Mahjoub, Mohamed Ali; Cléder, Catherine

doi:10.5772/intechopen.84856

Cited by 73 publications

(30 citation statements)

References 28 publications

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“…In [94], RNN, SVM, and MLR (multivariable linear regression) are compared. RNN performed better than SVM and MLR with 94.1% accuracy for Spanish databases and 83.42% for Emo-DB.…”

Section: Review Of Speech Emotion Recognitionmentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and correlate emotional aspects of speech signals are quite contrasting quantitatively and qualitatively, which present enormous difficulties in blending knowledge from interdisciplinary fields, particularly speech emotion recognition, applied psychology, and human-computer interface. The paper carefully identifies and synthesizes recent relevant literature related to the SER systems' varied design components/methodologies, thereby providing readers with a state-of-the-art understanding of the hot research topic. Furthermore, while scrutinizing the current state of understanding on SER systems, the research gap's prominence has been sketched out for consideration and analysis by other related researchers, institutions, and regulatory bodies.

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“…In [94], RNN, SVM, and MLR (multivariable linear regression) are compared. RNN performed better than SVM and MLR with 94.1% accuracy for Spanish databases and 83.42% for Emo-DB.…”

Section: Review Of Speech Emotion Recognitionmentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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“…Moreover, 71% of customers are willing to recommend that method of selling products and services. According to experts, optimization of the FCR indicator is among the most essential requirements related to social media-based customer service [29]. Additionally, the need to utilize this communication channel is supported by analyses [30], which show that by failing to use social media channels companies run a risk of losing as many as 15% of their current customers.…”

Section: B Internet and Smartphonesmentioning

confidence: 99%

Influence of the Contact Center Systems Development on Key Performance Indicators

Płaza

Pawlik²

2021

IEEE Access

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The role played by information and communication technologies in human life is growing by the year. During the current pandemic, communication systems have become an essential part of operations of many industries around the world. This has also brought a dynamic development of solutions implemented in Contact Center systems, which ensure quick contact with customers. This paper presents how the pace of the development of Contact Center systems affects optimization of selected Key Performance Indicators (KPIs). It contains a detailed analysis of how successive technologies deployed in the industry, including IVR, chat, WebRTC, Visual IVR, Social Media, MultiChannel, OmniChannel and bots, have affected KPIs. The authors selected essential indicators that are critical to customer service, namely Service Level, Cost per Contact, Customer Satisfaction, Average Handle Time, First Call Resolution, Abandon Rate, Average Waiting Time, and Occupancy Rate. The authors also presented possible trends in the development of the solutions discussed in this paper, including in particular broad possibilities of employing artificial intelligence. AI methods will soon enable equipping Contact Centers with mechanisms for multi-criteria content classification and smart emotion recognition and behavioral profiling methods, and will optimize the process of predicting both inbound and outbound traffic.

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“…intensity, emotions, polarity and subjectivity. Kerkeni et al ( 2019 ) have defined the emotional health analysis through speech as a four step process. These four steps are as follows: Collection of voice samples Feature extraction Feature selection eClassification …”

Section: Emotional Health Analysismentioning

confidence: 99%

Employing Machine Learning for Multi-perspective Emotional Health Analysis

Mangla

Akhare

Deokar

et al. 2020

Emotion and Information Processing

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Human emotion is undoubtedly the most fundamental aspect of human relationships and thus guides human to distinguish among right and wrong human behavior. Also, emotional equilibrium governs the human happiness state which indirectly affects physical health. In the absence of affective emotional equilibrium, unhappiness and common mental disorders may emerge. Despite the powerful influence of emotion on human behavior, it lacks systematic research and has been confined to fixation of beliefs only. This lack of recognition shows an ignorant attitude toward emotional health leading to unhappiness among humans. Here, in this chapter, the authors aim to present the importance of emotional health analysis. Additionally, they discuss the various perspective in order to evaluate the emotional health of humans.

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Automatic Speech Emotion Recognition Using Machine Learning

Cited by 73 publications

References 28 publications

A Comprehensive Review of Speech Emotion Recognition Systems

A Comprehensive Review of Speech Emotion Recognition Systems

Influence of the Contact Center Systems Development on Key Performance Indicators

Employing Machine Learning for Multi-perspective Emotional Health Analysis

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