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Fig, | Automated Speech emotion detection Speech is the most common form of communication which is rich in paralinguistic information, to convey emotion, age, gender and other attributes in real-time. From the past few decades, Speech Emotion Recognition (SER) has developed intoan interesting research area of Computer Science related to smart home automation, social media, education, health care, and a variety of other Artificial Intelligence (AI) based applications. Fig. 1 shows a simple setup for automated SER. One of the most challenging steps of SER is the feature generation for emotions, because the features derived from raw speech signals will be able to effectively distinguish emotion states [6]. Speech emotion recognition is a process of identifying human emotions from a recorded speech or in real time through theuse of advanced technologies, algorithms, and accurate datasets to train the machine or system to detect and classify these emotions based on the words used or tone of the voice. Fig. 2 shows the Architectural components of an ideal SER system. Due to the gap or disparity amongst Acoustic characteristics (intensity and frequency pattern of sound) and Human emotions (happy, sad, etc), automated Speech Emotion Recognition is a challenging procedure, which depends greatly on the distinguishable acoustic characteristics captured from a specified recognition task.

Figure 1 , | Automated Speech emotion detection Speech is the most common form of communication which is rich in paralinguistic information, to convey emotion, age, gender and other attributes in real-time. From the past few decades, Speech Emotion Recognition (SER) has developed intoan interesting research area of Computer Science related to smart home automation, social media, education, health care, and a variety of other Artificial Intelligence (AI) based applications. Fig. 1 shows a simple setup for automated SER. One of the most challenging steps of SER is the feature generation for emotions, because the features derived from raw speech signals will be able to effectively distinguish emotion states [6]. Speech emotion recognition is a process of identifying human emotions from a recorded speech or in real time through theuse of advanced technologies, algorithms, and accurate datasets to train the machine or system to detect and classify these emotions based on the words used or tone of the voice. Fig. 2 shows the Architectural components of an ideal SER system. Due to the gap or disparity amongst Acoustic characteristics (intensity and frequency pattern of sound) and Human emotions (happy, sad, etc), automated Speech Emotion Recognition is a challenging procedure, which depends greatly on the distinguishable acoustic characteristics captured from a specified recognition task.

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Fig. 3 Deep Stride CNN design for speech emotion recognition Convolutional Neural Network (CNN) consists of convolution layers, pooling layers, fully connected layers, and a SoftMax unit; this sequential network forms a feature extraction. Initially, input spectrograms are convolved with different filters during the training phase and feature maps are obtained. Polling layers accumulate maximum activation functions from the feature maps, to reduce their dimensionality. Lastly, SoftMax unit performs the task of classification.
Fig. 3 Deep Stride CNN design for speech emotion recognition Convolutional Neural Network (CNN) consists of convolution layers, pooling layers, fully connected layers, and a SoftMax unit; this sequential network forms a feature extraction. Initially, input spectrograms are convolved with different filters during the training phase and feature maps are obtained. Polling layers accumulate maximum activation functions from the feature maps, to reduce their dimensionality. Lastly, SoftMax unit performs the task of classification.
Related topics:
Machine LearningEmotion Recognition from Speech
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