Results 271 to 280 of about 142,938 (289)

Maximum F1-Score Discriminative Training Criterion for Automatic Mispronunciation Detection

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015
We carry out an in-depth investigation on a newly proposed Maximum F1-score Criterion (MFC) discriminative training objective function for Goodness of Pronunciation (GOP) based automatic mispronunciation detection that makes use of Gaussian Mixture Model-hidden Markov model (GMM-HMM) as acoustic models. The formulation of MFC seeks to directly optimize
Hao Huang, Haihua Xu, Xianhui Wang, Wushour Silamu   +3 more
openaire   +1 more source

A Shape Comparison Reinforcement Method Based on Feature Extractors and F1-Score

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019
Evaluating object segmentation is a topic of great interest for shape comparison techniques. In this work, ad-hoc metrics for a detailed segmentation analysis and a novel keypoint based method for comparing pairs of shapes are presented. As references, two different segmentation approaches were used: a handmade segmentation and an automatic one based ...
Avola, D, Cinque, L, Foresti, GL, Lamacchia, F, Marini, MR, Perini, L, Qorraj, K, Telesca, G   +7 more
openaire   +2 more sources

F1 Score Assesment of Gaussian Mixture Background Subtraction Algorithms Using the MuHAVi Dataset

6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15), 2015
Background subtraction algorithms are mainly used to segment some specific moving objects in an image sequences. Within of the action recognition context, these methods may be proper to generate automatically silhouettes of the human actions. In this way, MuHAVi is a human action dataset which provides a small set of manually annotated silhouettes and ...
J. Sepúlveda, S.A. Velastin
openaire   +1 more source

Beyond Balanced Accuracy: Calibrated F1-Score for Reliable AI Evaluation in Imbalanced Domains

Evaluating the performance of Artificial Intelligence (AI) models in imbalanced domains poses significant challenges. Traditional metrics like accuracy can be misleading, favoring the majority class and masking poor performance on the minority class, which is often of greater interest.
Revista, Zen, IA, 10
openaire   +1 more source

Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach

Food Chemistry
Individual detection techniques cannot guarantee accurate and reliable results when combatting the presence of adulterated lamb meat in the market. Here, we propose an approach combining the electronic nose and near-infrared spectroscopy fusion data with machine learning methods to effectively detect adulterated lamb meat (mixed with duck meat).
Wenshen Jia, Yingdong Qin, Changtong Zhao   +2 more
openaire   +2 more sources

On kNN Class Weights for Optimising G-Mean and F1-Score

2023
Grzegorz Góra, Andrzej Skowron
openaire   +1 more source

Maximum F1-Score Training for End-to-End Mispronunciation Detection and Diagnosis of L2 English Speech

2022 IEEE International Conference on Multimedia and Expo (ICME), 2022
Bi-Cheng Yan, Hsin-Wei Wang, Shao-Wei Fan Jiang, Fu-An Chao, Berlin Chen   +4 more
openaire   +1 more source

Evaluating compressive sensing algorithms in through-the-wall radar via F1-score

International Journal of Signal and Imaging Systems Engineering, 2018
Ali A. AlBeladi, Ali H. Muqaibel
openaire   +1 more source

A Comprehensive Investigation of Machine Learning Algorithms with SMOTE Integration to Maximize F1 Score

2023
Surbhi Sharma, Alka Singhal
openaire   +1 more source

Maximum F1-score discriminative training for automatic mispronunciation detection in computer-assisted language learning

Interspeech 2012, 2012
Hao Huang, Jianming Wang, Halidan Abudureyimu   +2 more
openaire   +1 more source