This paper introduces a new English speech dataset suitable for training and evaluating speaker recognition systems. Samples were obtained from non-native English speakers from the Arab region over the course of two months. The dataset was divided into two sub-datasets. Ten samples were collected from each speaker for each sub-dataset. The first sub-dataset contains samples of speakers repeating the phrase "Machine learning 1, 2, 3, 4, 5, 6, 7, 8, 9, 10". The second sub-dataset contains samples for the same speakers speaking randomly for five to ten seconds for each sample. The dataset consists of 150 speakers with a total of 3,000 data samples and about six hours of speech.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958529 | PMC |
| http://dx.doi.org/10.1016/j.dib.2022.108070 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A new Q-matrix validation method based on signal detection theory.
Br J Math Stat Psychol
November 2024
Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
The Q-matrix is a crucial component of cognitive diagnostic theory and an important basis for the research and practical application of cognitive diagnosis. In practice, the Q-matrix is typically developed by domain experts and may contain some misspecifications, so it needs to be refined using Q-matrix validation methods. Based on signal detection theory, this paper puts forward a new Q-matrix validation method (i.
View Article and Find Full Text PDFMixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model.
Comput Methods Programs Biomed
February 2024
School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia. Electronic address:
Article Synopsis
- Sleep staging is crucial for diagnosing sleep disorders, and automating this process can save time and improve accuracy for experts.
- A new model called MixSleepNet combines 3D convolutional and graph convolutional techniques to analyze various physiological signals (EEG, EMG, EOG, ECG) for better sleep stage classification.
- The model demonstrated high accuracy, with scores around 0.830 and 0.812 for different datasets, proving its effectiveness compared to expert assessments.
Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.
J Neural Eng
November 2023
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.
Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.
View Article and Find Full Text PDFNutrition Therapy by Nutrition Support Team: A Comparison of Multi-Chamber Bag and Customized Parenteral Nutrition in Hospitalized Patients.
Nutrients
May 2023
College of Pharmacy and Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan-si 15588, Gyeonggi-do, Republic of Korea.
This study aimed to investigate the activity of a nutrition support team (NST) and the trends of multi-chamber bag (MCB) and customized parenteral nutrition (PN) with NST consultations in South Korea. Data were obtained from the National Inpatient Sample Cohort between 2015 and 2020. Three datasets were constructed for NST consultation, MCB-PN product prescriptions, and aseptic preparation of total PN.
View Article and Find Full Text PDFThe morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as "narrow,""equal,""capping" or "fusion" by three observers.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!