Applying Machine Learning Techniques for Speech Emotion Recognition

Tarunika, K.; Pradeeba, R.B; Aruna, P.

doi:10.1109/icccnt.2018.8494104

Cited by 29 publications

(12 citation statements)

References 4 publications

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“…Apart from these, there are number of other research work on handwriting [14], relating handwriting to depression and anxiety [22], handwriting to personality [8,23], combination of audio-video-body gestures [2,10,11]. To the best of our knowledge this is the first approach for recognition of emotion using the combination of psychological assessment with handwriting analysis using deep learning.…”

Section: Related Workmentioning

confidence: 99%

“…Tarunika et. al [10] have implemented Deep Neural Network (DNN) and knearest neighbor (k-NN) for emotion recognition using audio, particularly when the person's mind is in scary state. For detection of emotion of an individual from the video, the characteristics analyzed are facial expressions, movements and activities, using machine learning.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Emotional State Based On Handwriting Analysis and Psychological Assessment

Kedar*,

Rokade

2019

IJEAT

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Emotions describe the physiological states of an individual and are generated subconsciously. They motivate, organize, and guide perception, thought, and action. Emotions can be positive or negative. Negative emotions manifest in the form of depression, anxiety and stress. It is necessary to identify negative emotions of an individual who might be in the need for counseling or psychological treatment. Body signal analysis, handwriting analysis, and psychological assessment are some mechanisms to measure them. In this paper, emotional state is being measured through the person’s handwriting sample analysis and psychological assessment. Psychological assessment is done by using the results of DASS questionnaire attempted by the individual. Convolutional Neural Network (CNN) algorithm is used to find the emotional state of an individual from his/her handwriting sample. Comparative analysis is performed to suggest counseling/medication if required. The final CNN model is formed by using the ensemble method over cross-validation models. The accuracy achieved by the CNN model over the test dataset is 91.25%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition of Emotional State Based On Handwriting Analysis and Psychological Assessment

Kedar*,

Rokade

2019

IJEAT

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“…Besides, we demonstrate the connection among Kernal and pooling size of the 1-d layers of our model, and window and step estimate for conventional sound highlight like MFCCs. K. Tarunika [5] .three evolutions are using for cross-corpus setup and one corpus evolution on the IEMOCAP dataset. both cases we watch an improvement in the classification execution using the adversarial techniques.…”

Section: Knowledge-based Techniques:-mentioning

confidence: 99%

Design & Development of Network Geo-Fencing Model for User Monitoring and it’s Alertness in a Security Applications

Barapatre¹,

Deshmukh²

2019

IJRAT

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Communication plays a vital role according to the people's emotion, as emotions and gesture play 80% role while communication. Nowadays emotion recognition and classification are used in different areas to understand the human feelings like in the robotics, Health care, Military, Home automation, Hands-free computing, Mobile Telephony, Video game,call-center system, Marketing, etc. SER can help better interaction between the machine and the human. There are various algorithms and combination of the algorithms are available to recognize and classify the audio according to their emotion. In this paper, we attempted to investigate the episodic significant works, their technique and the impact of the approaches and the scope of the correction of the results.

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“…The classification is a fundamental stage in the SER systems. Different classifiers have been exploited for SER such as Gaussian mixture models (GMMs) [19], K-nearest Neighbors (KNN) [20][21][22], Support Vector Machines (SVM) [20] and Deep Neural Network (DNN) [22].…”

Section: Introductionmentioning

confidence: 99%