Robust Multi-Scenario Speech-Based Emotion Recognition System

Zhu-Zhou, Fangfang; Gil-Pita, Roberto; García-Gómez, Joaquín; Rosa-Zurera, Manuel

doi:10.3390/s22062343

Cited by 12 publications

(4 citation statements)

References 51 publications

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“…However, most of the emotion recognition studies are conducted either by extracting specific parameters that become apparent when emotion is evoked (e.g., facial, voice or physiological indicators) [28,29], or by training the data to predict an emotion by applying mathematical algorithms [11,30]. Also, it is important to understand that non-verbal sound is processed differently than verbal sound/ speech in the brain and is relatively natural and universal means of human communication with higher cues for emotional responses [31,32].…”

Section: Related Workmentioning

confidence: 99%

A Preliminary Investigation on Frequency Dependant Cues for Human Emotions

2022

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The recent advances in Human-Computer Interaction and Artificial Intelligence have significantly increased the importance of identifying human emotions from different sensory cues. Hence, understanding the underlying relationships between emotions and sensory cues have become a subject of study in many fields including Acoustics, Psychology, Psychiatry, Neuroscience and Biochemistry. This work is a preliminary step towards investigating cues for human emotion on a fundamental level by aiming to establish relationships between tonal frequencies of sound and emotions. For that, an online perception test is conducted, in which participants are asked to rate the perceived emotions corresponding to each tone. The results show that a crossover point for four primary emotions lies in the frequency range of 417–440 Hz, thus consolidating the hypothesis that the frequency range of 432–440 Hz is neutral from human emotion perspective. It is also observed that the frequency dependant relationships between emotion pairs Happy—Sad, and Anger—Calm are approximately mirrored symmetric in nature.

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Section: Related Workmentioning

confidence: 99%

A Preliminary Investigation on Frequency Dependant Cues for Human Emotions

2022

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“…Thus, our daily actions usually depend on the emotional expressions of others [8]. Therefore, the expressions can be evaluated considering various factors, such as face expression [9], speech [10], and electroencephalogram (EEG) [11]. Among these highlights, the confront talks are one of the most prevalent for many reasons.…”

Section: Introductionmentioning

confidence: 99%

Facial Emotions Detection using an Efficient Neural Architecture Search Network

Islam

Mahum

Al-Salman

2023

Preprint

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Facial emotion detection is a challenging task that deals with emotion recognition. It has applications in various domains, such as behavior analysis, surveillance systems and human-computer interaction (HCI). Numerous studies have been implemented to detect emotions, including classical machine learning algorithms and advanced deep learning algorithms. For the machine learning algorithm, the hand-crafted feature needs to be extracted, which is a tiring task and requires human effort. Whereas in deep learning models, automated feature extraction is employed from samples. Therefore, in this study, we have proposed a novel and efficient deep learning model based on Neural Architecture Search Network utilizing superior artificial networks such as RNN and child networks. We performed the training utilizing the FER 2013 dataset comprising seven classes: happy, angry, neutral, sad, surprise, fear, and disgust. Furthermore, we analyzed the robustness of the proposed model on CK + datasets and comparing with existing techniques. Due to the implication of reinforcement learning in the network, most representative features are extracted from the sample network. It extracts all key features without losing the key information. Our proposed model is based on one stage classifier and performs efficient classification. Our technique outperformed the existing models attaining an accuracy of 98.14%, recall of 97.57%, and precision of 97.84%.

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“…Thus, our daily actions usually depend on the emotional expressions of others [8]. Therefore, the expressions can be evaluated considering various factors, such as face expression [9], speech [10], and electroencephalogram (EEG) [11]. The confront talks are among the most prevalent among these highlights for many reasons.…”

Section: Introductionmentioning

confidence: 99%

Facial Emotions Detection using an Efficient Neural Architecture Search Network

Islam,

Mahum,

AlSalman

et al. 2023

Preprint

View full text Add to dashboard Cite

Facial emotion detection is a challenging task that deals with emotion recognition. It has applications in various domains, such as behavior analysis, surveillance systems and human-computer interaction (HCI). Numerous studies have been implemented to detect emotions, including classical machine learning algorithms and advanced deep learning algorithms. For the machine learning algorithm, the hand-crafted feature needs to be extracted, which is a tiring task and requires human effort. Whereas in deep learning models, automated feature extraction is employed from samples. Therefore, in this study, we have proposed a novel and efficient deep learning model based on Neural Architecture Search Network utilizing superior artificial networks such as RNN and child networks. We performed the training utilizing the FER 2013 dataset comprising seven classes: happy, angry, neutral, sad, surprise, fear, and disgust. Furthermore, we analyzed the robustness of the proposed model on CK+ datasets and comparing with existing techniques. Due to the implication of reinforcement learning in the network, most representative features are extracted from the sample network. It extracts all key features without losing the key information. Our proposed model is based on one stage classifier and performs efficient classification. Our technique outperformed the existing models attaining an accuracy of 98.14%, recall of 97.57%, and precision of 97.84%.

show abstract

Robust Multi-Scenario Speech-Based Emotion Recognition System

Cited by 12 publications

References 51 publications

A Preliminary Investigation on Frequency Dependant Cues for Human Emotions

A Preliminary Investigation on Frequency Dependant Cues for Human Emotions

Facial Emotions Detection using an Efficient Neural Architecture Search Network

Facial Emotions Detection using an Efficient Neural Architecture Search Network

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