Work

Fine-Tuning VGG19 with Mel Spectrograms for Amazigh Spoken Digit Recognition

Year: 2026

Type: article

Abstract: This paper shows an improvement in speech recognition performance for the Amazigh language using the VGG19 model. We utilized a database of the first ten spoken Amazigh numbers, and we employed mel spectrograms as the visual representation of the audio. In the initial experiment, we froze the feature extraction layers of the VGG19, and trained only the custom classification layers, under the same training conditions as our previous work. This approach resulted in a recognition accuracy of 75.55%. Then, fine-tuning the entire model over 10 epochs improved the recognition accuracy to 89.33%.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/epjconf/202637402002/pdf

Authors: Hossam Boulal, Abdelkader Benzirar, Mohamed Hamidi, Jamal Barkani, Abdelouahed Essahlaoui, Mustapha Abarkan

Institution: Université Moulay Ismail de Meknes

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7166297745

FWCI:

Cites: 8

Cited by:

Related to: 10