{"id":{"repo_id":"vilnius","oai_identifier":"oai:vu.lt:elaba:31276456"},"canonical_url":"https://search.dev.ndltd.org/etd/vilnius/oai:vu.lt:elaba:31276456","repository":{"repo_id":"vilnius","name":"Vilnius University","base_url":"https://epublications.vu.lt/oai"},"display":{"title":"Research on the accuracy of Lithuanian speaker’s identification using recurrent neural networks /","abstract":"One of the main tasks of this thesis is to analyze the process of speaker identification by one’s voice by classifying a set of speaker’s voice features extracted from one’s voice sample. This thesis presents methods that are used to identify a speaker by voice. The process of speaker identification consists of several stages. Based on the research in the field, it was concluded that the most popular and commonly used method for speaker feature extraction is Mel frequency cepstral coefficients and its derivatives. 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