A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers
Abstract Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. Despite progress, achieving high accuracy in multi-class classification remains challenging. This paper proposes a novel hy...
Saved in:
| Main Authors: | Mehtab Ur Rahman, Cem Direkoglu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-025-02978-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Leveraging LSTM and ensemble classifiers for enhanced food waste classification
by: Khalaf Alsalem
Published: (2025-05-01) -
Enhancing Voice Spoofing Detection: A Hybrid Approach With VGGish-LSTM Model for Improved Security in Automatic Speaker Verification Systems
by: Komal Shahzad, et al.
Published: (2025-01-01) -
SMOTEHashBoost: Ensemble Algorithm for Imbalanced Dataset Pattern Classification
by: Seema Yadav, et al.
Published: (2025-01-01) -
A Network Traffic Classification Method for Class-Imbalanced Data
by: Xiaohui Guan, et al.
Published: (2015-06-01) -
Facial expression using Histogram of Oriented Gradients and Ensemble Classifier
by: Maher Kh. Hussien, et al.
Published: (2022-11-01)