Speech Emotion Detection using IoT based Deep Learning for Health Care

Tariq, Zeenat; Shah, Sayed Khushal; Lee, Yugyung

doi:10.1109/bigdata47090.2019.9005638

Cited by 43 publications

(17 citation statements)

References 17 publications

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“…In the mainstream literature, descriptions of the hardware on which a neural network is trained or executed are scarce. However, Tariq et al (2019) describe that neural networks -especially deep neural networks -are trained and run in cloud-like data centers. The locally collected data is transferred to these servers, deleted on the local device, processed on the servers, and only the result is sent back to the end device.…”

Section: Related Workmentioning

confidence: 99%

“…Besides MobileNetV2, the cited papers do not specify which machine has been used to generate results. Therefore, for the time being, it is assumed that the results of the CNNs were generated on cloud-like servers, similar to what is described byTariq et al (2019). Since a direct comparison of server-generated results with terminal device-generated results is not possible, the subsequent interpretation of the results of this study is limited.Especially for neural networks, there are other metrics for measuring and evaluating the training results.Here, the result is generally given in terms of training and validation accuracy and duration of training and validation losses, respectively.…”

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confidence: 99%

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Edge emotion recognition: applying fast Fourier transform on speech Mel spectrograms to classify emotion on a Raspberry Pi for near real-time analytics

Andrade

Buchkremer

2022

Preprint

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Many people and machines are inherently unable to interpret socio-affective cues such as tone of voice. Thoughtful adoption of intelligent technologies may improve the conversation. Since direct communication often occurs via edge devices, where an additional network connection is often not guaranteed, we now describe a real-time processing method that captures and evaluates emotions in a speech via a terminal device such as the Raspberry Pi computer. In this article, we also present the current state of research on speech emotional recognition. We examine audio files from five important emotional speech databases and visualize them in situ with dB-scaled Mel spectrograms using TensorFlow and Matplotlib. Audio files are transformed using the fast Fourier transform method to generate spectrograms. For classification, a support vector machine kernel and a CNN with transfer learning are selected. The accuracy of this classification is 70% and 77%, respectively, a good value related to the execution of the algorithms on an edge device instead of on a server. On a Raspberry Pi, it took less than one second to evaluate pure emotion in speech using machine learning and the corresponding visualization, suggesting the speaker's emotional state.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Edge emotion recognition: applying fast Fourier transform on speech Mel spectrograms to classify emotion on a Raspberry Pi for near real-time analytics

Andrade

Buchkremer

2022

Preprint

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“…In [12], the authors presented a method for detecting emotion using speech using IoT based deep learning. The authors implemented a real time system based on IoT, and then classified emotions.…”

Section: Study Of Speech Emotion Detectionmentioning

confidence: 99%

Bangla Speech Emotion Detection using Machine Learning Ensemble Methods

Ayon¹,

Rabbi²,

Habiba³

et al. 2022

Adv. sci. technol. eng. syst. j.

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Emotion is the most important component of being human, and very essential for everyday activities, such as the interaction between people, decision making, and learning. In order to adapt to the COVID-19 pandemic situation, most of the academic institutions relied on online video conferencing platforms to continue educational activities. Due to low bandwidth in many developing countries, educational activities are being mostly carried out through audio interaction. Recognizing an emotion from audio interaction is important when video interaction is limited or unavailable. The literature has documented several studies on detection of emotion in Bangla text and audio speech data. In this paper, ensemble machine learning methods are used to improve the performance of emotion detection from speech data extracted from audio data. The ensemble learning system consists of several base classifiers, each of which is trained with both spontaneous emotional speech and acted emotional speech data. Several trials with different ensemble learning methods are compared to show how these methods can yield an improvement over traditional machine learning method. The experimental results show the accuracy of ensemble learning methods; 84.37% accuracy was achieved using the ensemble learning with bootstrap aggregation and voting method.

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“…Methods Used [1] Regularized non-negative matrix factorization (NMF) problem with a regularization [2] Modeling source separation using MRFs(Markov random fields), MRF inference [3] Row mean vector of the spectrograms, Euclidean distance and Manhattan distance, [4] Deep CNN [5] Hilbert-Huang transform (HHT) and Teager Energy Operator (TEO) [6] Single-Pass Spectrogram Inversion (SPSI) algorithm, the magnitude spectra using quadratic interpolation, Griffin-Lim algorithm. [7] Spectrograms and Deep Convolutional Neural Network (D-CNN) [8] Data Synchronization Management and Automatic Evaluation [9] Feature extraction, Image edge detection [10] Enhanced kernel isometric mapping (EKIsomap) [11] SVM, CNN [12] discrete category and Dimensional structure theories discrete category and Dimensional structure theories discrete category and Dimensional structure theories discrete category and Dimensional structure theories Discrete category and dimensional structure theory [13] Hidden Markov Model (HMM), Gaussian Mixtures Model (GMM), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-nearest neighbor (KNN) [14] Firefly Algorithm [15] Data normalization and data augmentation techniques [16] Attribute cryptosystem and blockchain technology [17] Novel fast convolution algorithms [18] Batch Normalization, Max Pooling, ReLU Activation Function [19] GMM, HMM models [20] Support Vector Machine…”

Section: Yearmentioning

confidence: 99%

An Enhanced CNN-2D for Audio-Visual Emotion Recognition (AVER) Using ADAM Optimizer

Indira¹

2021

TURCOMAT

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The importance of integrating visual components into the speech recognition process for improving robustness has been identified by recent developments in audio visual emotion recognition (AVER). Visual characteristics have a strong potential to boost the accuracy of current techniques for speech recognition and have become increasingly important when modelling speech recognizers. CNN is very good to work with images. An audio file can be converted into image file like a spectrogram with good frequency to extract hidden knowledge. This paper provides a method for emotional expression recognition using Spectrograms and CNN-2D. Spectrograms formed from the signals of speech it’s a CNN-2D input. The proposed model, which consists of three layers of CNN and those are convolution layers, pooling layers and fully connected layers extract discriminatory characteristics from the representations of spectrograms and for the seven feelings, performance estimates. This article compares the output with the existing SER using audio files and CNN. The accuracy is improved by 6.5% when CNN-2D is used.

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Speech Emotion Detection using IoT based Deep Learning for Health Care

Cited by 43 publications

References 17 publications

Edge emotion recognition: applying fast Fourier transform on speech Mel spectrograms to classify emotion on a Raspberry Pi for near real-time analytics

Edge emotion recognition: applying fast Fourier transform on speech Mel spectrograms to classify emotion on a Raspberry Pi for near real-time analytics

Bangla Speech Emotion Detection using Machine Learning Ensemble Methods

An Enhanced CNN-2D for Audio-Visual Emotion Recognition (AVER) Using ADAM Optimizer

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