Detection of Speech Signal for Speaker Recognition in Biometric Systems Using Optical Scanning Holography with Double Random Phase Encoding

In recent years, the speaker recognition technology is significant system for securing the biometric authentication and enabling remote logins for telephone banking and enterprise applications. Whereas Speech Signal Detection ensure with accurate speaker identification and verification for restricted access and sensitive data protection. The combination of speaker recognition technology with speech signal detection has accompanied in a new era of biometric authentication, providing unparalleled security and accuracy in verifying identities. However, Vulnerability to voice spoofing attacks leads to imitation of speaker such as mimicry or synthetic speech, compromise system security. To overcome these drawbacks, an Optical Scanning Holography with Double Random Phase Encoding (OSH-DRPE) is proposed using Texas Instruments Massachusetts Institute of Technology (TIMIT) dataset. After collecting the data, the data is preprocessed through normalization approaches for normalize the data. Then, a Mel-Frequency Cepstral Coefficients (MFCC) is employed for feature extraction to extract relevant features, which produces low computational complexity. At last, OSH-DRPE is used as classifier to detect speech signal and speaker recognition with higher accuracy, efficient computation and flexibility. From the results, it clearly shows that the proposed OSH-DRPE obtained higher values in the context of precision, F1 score and accuracy when compared to existing Hidden Markov Model (HMM).