Speaker Recognition Thesis

That was an important moment as we got to meet key players we have cooperated with since.In 2005, we participated in an international evaluation of language identification held by National Institute of Standards and Technology (NIST), US, which was a great success for us.

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Thanks to automatization, I also got to speaker voice recognition, which became the topic of my thesis.

I met the Phonexia co-founder Petr thanks to the fact too, as I often needed to consult him about using techniques I wanted to add to mine. The offer of innovative projects, travelling, internships and working abroad was very alluring, in addition to sparing me the one year of military service.

In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel.

The identification of a person through his voice within a forensic quality context is challenging.

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].In general, speaker recognition, like other bioinformatics features, is used to discriminate people through their voice.Automatic speaker recognition can be classified into two tasks: speaker verification and identification.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. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects.In speaker identification, the identity of a speaker is determined by analyzing and comparing the speech of unknown speaker with that of a known speaker.Speaker verification is the process of accepting or rejecting the identity claim of a speaker.Drygajlo [5] stated that “FSR is an established term used when automatic speaker recognition methods are adapted in forensic applications.” In [6] an FSR method using phonemes is described.The study used a combined method between the Gaussian mixture model (GMM) and hidden Markov model: Gaussian hidden Markov model.


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