Shallow Learning Versus Deep Learning in Speech Recognition Applications

Shallow learning and deep learning are two often employed methods in developing speech recognition applications. While shallow learning relies on conventional machine learning methods, deep learning utilises artificial neural networks to acquire knowledge from data. This research examines the disparities between the two strategies employed in speech recognition programmes. We analyse the benefits and limitations of each technique and evaluate their overall effectiveness in terms of accuracy and speed. Furthermore, we discuss the challenges and potential advancements of both methods in enhancing voice recognition systems. An understanding of the advantages and limitations of shallow learning and deep learning can facilitate the development of more efficient and accurate voice recognition applications for many areas.