Speaker Recognition Thesis

Speaker Recognition Thesis-72
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.That was probably the biggest impulse and a true eye-opener.

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.That was probably the biggest impulse and a true eye-opener.

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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.

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.

For many years, law enforcement agencies, lawyers, and judges have used voice forensic authentication to recognize suspects [2].

The identification of a person through speech samples with a forensic quality is challenging. 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 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.During my first year as a Ph D student, I met Honza Černocký, who had supervised Petr Schwarz’s thesis, and I started co-operating with his group fully.That resulted in a year-long internship together with Peter at the Oregon Graduate Institute (OGI), US, which was supervised by world-renowned Hynek Heřmanský.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.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.

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