Speaker Recognition Thesis

There are several applications of automatic speaker recognition that can be divided into commercial applications, such as voicemail, telephone banking, biometric authentication, and forensic applications [3].

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Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.

In a law court, science and technology are used in investigations to establish forensic evidences [1].

This diversity facilitates its usage in forensic experimentations.

Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling.

This resulted in a much larger interest in our group. The idea was to create a company that would cooperate with VUT to integrate technologies into the commercial sector.

It would also create new jobs in our city and, considering what we do, also help those who fight on the right side of the law.

What we managed to do within that one year was almost unbelievable.

And then, in 2003 and still under the auspices of OGI, we first introduced ourselves to the worldwide speech-recognition community.

In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results.

The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel.