No abstract

Given the increasing popularity of smartphones as all-in-one computing devices for corporate work and everyday personal use, it is no wonder that mobile devices have become the most appealing attack surface for today's cyber criminals. In that case obscene or harassing phone calls can be one of the most stressful and frightening invasions of privacy a person experiences. Thus Mobile security has become increasingly important in mobile computing. There exist various applications that block spam calls through the SIM card numbers by establishing a spam database which identities the source of income calls. But unfortunately, their effciency of work is not up to the mark, since its usually pointless to track and block the SIM card number, as the number of spam callers is constantly changed. Considering this point, we are presenting a new concept in which frauds will be recognized through their vocals, even in a noisy environment, with a few seconds of speech, as one can change his number several times but can't change his voice. Here we have used several algorithms and techniques, such as speaker verification, speaker identification, forensic speaker recognition (FSR), spectrogram masking, voice ltering, Mel-Frequency Cepstral Coeffcient (MFCC) and a combination of Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). Moreover, this system doesn't require any kind of personal information of the users. In this consequence, safety issues also remain in force. Findings of this study will be useful for lawyers, law enforcement agencies, and judges in the courts to recognize their suspects.