Speaker Recognition through Deep Learning Techniques

Abstract

Deep learning has now become an integral part of today's world and advancement in the field of deep learning has gained a huge development. Due to the extensive use and fast growth of deep learning, it has captured the attention of researchers in the field of speaker recognition. A detailed investigation regarding the process becomes essential and helpful to the researchers for designing robust applications in the field of speaker recognition, both in speaker verification and identification. This paper reviews the field of speaker recognition taking into consideration of deep learning advancement in the present era that boosts up this technology. The paper continues with a systematic review by firstly giving a basic idea of deep learning and its architecture with its field of application, then entering into the high-lighted portion of our paper i.e., speaker recognition which is one of the important applications of deep learning. Here we have mentioned its types, different processing techniques, challenges that come across in this technology, performance evaluation criteria, deep learning implementation frameworks, and lastly various databases used in the field of speaker identification (SI) and Speaker Verification (SV).

Speaker Identification Using Hybrid Subspace, Deep Learning and Machine Learning Classifiers

“…Many studies on speaker recognition have utilized deep learning and machine learning classifiers such as ANN, CNN, RNN, LSTM, SVM, KNN, HMM, and GMM [8,9]. Some of these classifiers are used to extract features, while others are used to identify speakers [10].…”