Comparative Examination of Spoken Language Recognition Through Deep Learning Algorithms – A Review

Abstract

This work investigates the area of spoken language recognition, which is essential to language translation and natural language processing. It examines existing models that have been trained on deep learning techniques, datasets, and performance metrics. The article presents a thorough comparative examination of spoken language identification methods using deep learning, highlighting their advantages and disadvantages. In order to help researchers develop new language identification models for speech signals, the effectiveness of spoken language models is examined. Additionally, a number of spoken language identification models were investigated using a variety of deep learning approaches, datasets, and performance indicators. This analysis focused on the key traits and challenges that these models face.

Keywords

• Spoken Language Identification, Gaussian Mixture Model, Convolutional Neural Network