Deep Learning Approaches for Speech Analysis: A Critical Insight

The main objective of speaker recognition is to identify the voice of an authenticated and authorized individual by extracting features from their voices. The number of published techniques for speaker recognition algorithms is text-dependent. On the other hand, text-independent speech recognition appears to be more advantageous since the user can freely interact with the system. Several scholars have suggested a variety of strategies for detecting speakers, although these systems were difficult and inaccurate. Relying on WOA and Bi-LSTM, this research suggested a text-independent speaker identification algorithm. In presence of various degradation and voice effects, the sample signals were obtained from a available dataset. Following that, MFCC features are extracted from these signals, but only the most important characteristics are chosen from the available features by utilizing WOA to build a single feature set. The Bi-LSTM network receives this feature set and uses it for training and testing. In the MATLAB simulation software, the proposed model’s performance is assessed and compared to that of the standard model. Various dependent factors, like accuracy, sensitivity, specificity, precision, recall, and Fscore, were used to calculate the simulated outputs. The findings showed that the suggested model is more efficient and precise at recognizing speaker voices.