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Found 11 papers, 0 papers with code
Speaker Identification from emotional and noisy speech data using learned voice segregation and Speech VGG
Speech signals are subjected to more acoustic interference and emotional factors than other signals.
Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments
The test results of the aforementioned hybrid models demonstrated that the proposed HMM-DNN leveraged the verification performance in emotional and stressful environments.
Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition
Using the Arabic Emirati-accented corpus, our results demonstrate that the proposed work yields average emotion recognition accuracy of 89. 3% compared to 84. 7%, 82. 2%, 69. 8%, 69. 2%, 53. 8%, 42. 6%, and 31. 9% based on CapsNet, CNN, support vector machine, multi-layer perceptron, k-nearest neighbor, radial basis function, and naive Bayes, respectively.
The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices
Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors.
COVID-19 Electrocardiograms Classification using CNN Models
In this study, a novel approach is proposed to automatically diagnose the COVID-19 by the utilization of Electrocardiogram (ECG) data with the integration of deep learning algorithms, specifically the Convolutional Neural Network (CNN) models.
CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions
This work aims at intensifying text-independent speaker identification performance in real application situations such as noisy and emotional talking conditions.
Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC
Recently there has been a formidable work which has been put up from the people who are working in the frontlines such as hospitals, clinics, and labs alongside researchers and scientists who are also putting tremendous efforts in the fight against COVID-19 pandemic.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Emotion Recognition Using Speaker Cues
This research aims at identifying the unknown emotion using speaker cues.
Emirati-Accented Speaker Identification in Stressful Talking Conditions
This research is dedicated to improving text-independent Emirati-accented speaker identification performance in stressful talking conditions using three distinct classifiers: First-Order Hidden Markov Models (HMM1s), Second-Order Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s).
Three-Stage Speaker Verification Architecture in Emotional Talking Environments
In this work, a three-stage speaker verification architecture has been proposed to enhance speaker verification performance in emotional environments.
Speaker Identification in each of the Neutral and Shouted Talking Environments based on Gender-Dependent Approach Using SPHMMs
It is well known that speaker identification performs extremely well in the neutral talking environments; however, the identification performance is declined sharply in the shouted talking environments.
