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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.
SID systems can perform well under matched training and test conditions but their performance degrades significantly because of the mismatch caused by background noise in real-world environments.
Achieving robustness to SID systems becomes an important research problem.
The results suggest that the extracted auditory feature vectors lead to much better performance, i.e.
higher SID accuracy, compared to the MFCC-based recognition system especially for low SNRs.
In this thesis, the possible benefits of adapting a biologically-inspired model of human auditory processing as part of the front-end of a SID system are examined.
This auditory model named Auditory Image Model (AIM) generates the stabilized auditory image (SAI). After all, all you need is to understand the core principle and the rest is piece of cake.Later, at high school of electric engineering, I learned about automatization, and I was riveted by it.That was probably the biggest impulse and a true eye-opener.There was nobody in the Czech republic dealing with top-level language and speaker recognition, even the working style was different, and we had to progress in our research much faster – basically on a daily basis.Finally, our research would not get shelved and gather dust as the academic and commercial spheres would become connected.Thus, my idea was one of synergy, friendship, research and helping the “good guys”.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 the second experimental part of this thesis, the developed version of the system is assessed for speaker data sets of different size.Clean speech is used for the training phase while speech in the presence of babble noise is used for speaker testing.