  Recognition systems are commonly designed to authenticate users at the access
control levels of a system. A number of voice recognition methods have been
developed using a pitch estimation process which are very vulnerable in low
Signal to Noise Ratio (SNR) environments thus, these programs fail to provide
the desired level of accuracy and robustness. Also, most text independent
speaker recognition programs are incapable of coping with unauthorized attempts
to gain access by tampering with the samples or reference database. The
proposed text-independent voice recognition system makes use of multilevel
cryptography to preserve data integrity while in transit or storage. Encryption
and decryption follow a transform based approach layered with pseudorandom
noise addition whereas for pitch detection, a modified version of the
autocorrelation pitch extraction algorithm is used. The experimental results
show that the proposed algorithm can decrypt the signal under test with
exponentially reducing Mean Square Error over an increasing range of SNR.
Further, it outperforms the conventional algorithms in actual identification
tasks even in noisy environments. The recognition rate thus obtained using the
proposed method is compared with other conventional methods used for speaker
identification.
