Speaker verification from partially encrypted compressed speech for forensic investigation

Speaker verification has recently been introduced to the forensic field as a new and complimentary approach to other forensic methods. With the advancement in speech communication technologies including voice over IP and wireless multimedia applications, speech is seldom sent between two parties in plain, it is at least partially encrypted before transmission. We present automatic speaker verification techniques based on hidden Markov and Gaussian mixture models from partially encrypted speech from the perceptually less relevant speech features which are unencrypted. An equal error rate (EER) of 23% and minimum detection cost value of 8% has been achieved on a database of 84 speakers using adapted Gaussian mixture modeling. Comparison between different modeling techniques and effect of Gaussian mixture densities are also carried out and results are tabulated. The results suggest that partial or selective encryption techniques may provide content protection but will not protect the speaker’s identity.