Results 21 to 30 of about 195,949 (260)

A Vector Quantized Masked Autoencoder for Speech Emotion Recognition [PDF]

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023
Recent years have seen remarkable progress in speech emotion recognition (SER), thanks to advances in deep learning techniques. However, the limited availability of labeled data remains a significant challenge in the field.
Samir Sadok, Simon Leglaive, Renaud S'eguier   +2 more
semanticscholar   +1 more source

Computer Speech Recognition Technology and Graphic Shape Design

Advances in Multimedia, 2022
In order to solve the problem of lack of multimodal emotional database, a computer speech recognition technology and graphic form design research were proposed.
Yuxi Niu
doaj   +1 more source

Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models

Sensors, 2021
The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions ...
Babak Joze Abbaschian, Daniel Sierra-Sosa, Adel Elmaghraby   +2 more
doaj   +1 more source

Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network

Applied Sciences, 2023
Speech emotion recognition (SER) is the process of predicting human emotions from audio signals using artificial intelligence (AI) techniques. SER technologies have a wide range of applications in areas such as psychology, medicine, education, and ...
A. Alluhaidan, Oumaima Saidani, Rashid Jahangir, M. Nauman, O. Neffati   +4 more
semanticscholar   +1 more source

Generative Emotional AI for Speech Emotion Recognition: The Case for Synthetic Emotional Speech Augmentation [PDF]

Applied Acoustics, 2023
Despite advances in deep learning, current state-of-the-art speech emotion recognition (SER) systems still have poor performance due to a lack of speech emotion datasets.
Abdullah Shahid, S. Latif, Junaid Qadir
semanticscholar   +1 more source

Speech Emotion Recognition Based on Voice Rhythm Differences [PDF]

Jisuanji kexue
Speech emotion recognition has an important application prospect in financial anti-fraud and other fields,but it is increasingly difficult to improve the accuracy of speech emotion recognition.The existing methods of speech emotion recognition based on ...
ZHANG Jiahao, ZHANG Zhaohui, YAN Qi, WANG Pengwei
doaj   +1 more source

Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer

Italian National Conference on Sensors, 2023
Speech emotion recognition (SER) is a challenging task in human–computer interaction (HCI) systems. One of the key challenges in speech emotion recognition is to extract the emotional features effectively from a speech utterance.
Rizwan Ullah, M. Asif, W. Shah, Fakhar Anjam, Ibrar Ullah, Tahir Khurshaid, L. Wuttisittikulkij, Shashi Shah, Syed Mansoor Ali, Mohammad Alibakhshikenari   +9 more
semanticscholar   +1 more source

Artificial Neural Network vs. Support Vector Machine For Speech Emotion Recognition

Tikrit Journal of Pure Science, 2023
Today, the subject of emotion recognition from speech got the attention of many researchers who are interested in the topic of speech recognition and it has engaged in many applications.
Mohamed. A. Ahmad
doaj   +1 more source

Speech Emotion Recognition Using Deep Learning Techniques: A Review

IEEE Access, 2019
Emotion recognition from speech signals is an important but challenging component of Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including ...
Ruhul Amin Khalil, Edward Jones, Mohammad Inayatullah Babar, Tariqullah Jan, Mohammad Haseeb Zafar, Thamer Alhussain   +5 more
doaj   +1 more source

Speech Emotion Recognition via CNN-Transformer and multidimensional attention mechanism [PDF]

Speech Communication
Speech Emotion Recognition (SER) is crucial in human-machine interactions. Mainstream approaches utilize Convolutional Neural Networks or Recurrent Neural Networks to learn local energy feature representations of speech segments from speech information ...
Xiaoyu Tang, Jiazheng Huang, Yixing Lin, Ting Dang, Jintao Cheng   +4 more
semanticscholar   +1 more source