Results 221 to 230 of about 195,949 (260)

Clinician's attitudes & perspective on chemical adherence testing in hypertension (CATCH): A qualitative interview study

British Journal of Clinical Pharmacology, EarlyView.
Abstract Aims Chemical Adherence Testing (CAT) is gaining prominence as a reliable and valid clinical method to detect whether antihypertensive agents are being taken as prescribed. This study aimed to explore clinicians' attitudes and perspectives on the clinical use of CAT.
Roshan Shahab, Louise Rabbitt, Gerard J. Molloy   +2 more
wiley   +1 more source

Empowering citizens to spontaneously report suspected adverse drug reaction: Systematic literature review of interventions and their impact

British Journal of Clinical Pharmacology, EarlyView.
This systematic literature review aimed to identify and characterize existing interventions designed to empower citizens to spontaneously report adverse drug reactions (ADRs) and to determine which interventions have been shown to be the most effective internationally. The research question was structured using the PICO framework.
Margarida Perdigão, Ana Margarida Advinha, Manuel José Lopes, Sofia de Oliveira Martins   +3 more
wiley   +1 more source

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Speech Emotion Recognition Using Deep Learning

International Conference on Computational Collective Intelligence, 2023
Speech emotion recognition is the task of automatically detecting the emotional state of a speaker from their spoken words. It is a growing area of research that has applications in various fields such as human computer interaction, education, and ...
Kartikeya Srinivas Chintalapudi, Irfan Ali Khan Patan, Harsha Vardhan Sontineni, Venkata Saroj Kushwanth Muvvala, S. Gangashetty, A. Dubey   +5 more
semanticscholar   +1 more source

Speech emotion recognition with deep convolutional neural networks

Biomedical Signal Processing and Control, 2020
The speech emotion recognition (or, classification) is one of the most challenging topics in data science. In this work, we introduce a new architecture, which extracts mel-frequency cepstral coefficients, chromagram, mel-scale spectrogram, Tonnetz ...
Dias Issa, M. Demirci, A. Yazıcı
semanticscholar   +1 more source

Dual-TBNet: Improving the Robustness of Speech Features via Dual-Transformer-BiLSTM for Speech Emotion Recognition

IEEE/ACM Transactions on Audio Speech and Language Processing, 2023
Speech emotion recognition has always been one of the topics that have attracted a lot of attention from many researchers. In traditional feature fusion methods, the speech features used only come from the data set, and the weak robustness of features ...
Zheng Liu, Xin Kang, Fuji Ren
semanticscholar   +1 more source

Speech emotion recognition using deep 1D & 2D CNN LSTM networks

Biomedical Signal Processing and Control, 2019
We aimed at learning deep emotion features to recognize speech emotion. Two convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D CNN LSTM network and one 2D CNN LSTM network, were constructed to learn local and global ...
Jianfeng Zhao, Xia Mao, Lijiang Chen
semanticscholar   +1 more source

The Acoustically Emotion-Aware Conversational Agent With Speech Emotion Recognition and Empathetic Responses

IEEE Transactions on Affective Computing, 2023
Emotion is important for the conversational user interface. In prior research, conversational agents (CAs) employ natural language process techniques to create affective interaction based on text. However, the use of acoustic features of speech for voice-
Jiaxiong Hu, Yun Huang, Xiaozhu Hu, Ying-Qing Xu   +3 more
semanticscholar   +1 more source

Speech emotion recognition using machine learning

AIP Conference Proceedings
– Speech Emotion Recognition (SER) system using machine learning techniques. It utilizes the RAVDESS dataset to identify emotions such as happiness, sadness, anger, and calm from speech.
Krishna Prakash Rajagopal, Absarkhan Kather Hussain, A. Veeramani, Gurusurya Meiyapparaj, Hariharan Ashokan   +4 more
semanticscholar   +1 more source

EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark

Interspeech
Speech emotion recognition (SER) is an important part of human-computer interaction, receiving extensive attention from both industry and academia. However, the current research field of SER has long suffered from the following problems: 1) There are few
Ziyang Ma, Mingjie Chen, Hezhao Zhang, Zhisheng Zheng, Wenxi Chen, Xiquan Li, Jiaxin Ye, Xie Chen, T. Hain   +8 more
semanticscholar   +1 more source

Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results

The Speaker and Language Recognition Workshop
The Odyssey 2024 Speech Emotion Recognition (SER) Challenge aims to enhance innovation in recognizing emotions from spontaneous speech, moving beyond traditional datasets derived from acted scenarios.
Lucas Goncalves, Ali N. Salman, Abinay Reddy Naini, Laureano Moro Velázquez, Thomas Thebaud, Leibny Paola, Najim Garcia, Berrak Dehak, Carlos Sisman, Busso   +9 more
semanticscholar   +1 more source