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Examining Feature Vector for Phoneme Recognition

The aim of this paper is to analyze usability of descriptors coming from music information retrieval to the phoneme analysis. The case study presented consists in several steps. First, a short overview of parameters utilized in speech analysis is given. Then, a set of time and frequency domain-based parameters is selected and discussed in the context of stop consonant acoustical characteristics. A toolbox created for this purpose in the Matlab environment is presented. The next analysis step includes the process of selecting the most discriminating descriptors based on Bron Kerbosch algorithm. It is shown that parameters resulted from this analysis can be used for separation of consonants. Finally, phoneme recognition is performed employing k-NN classifier. Keywords: Phoneme analysis, parametrization, phoneme recognition, k-NN classifier

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DOI
Digital Object Identifier link open in new tab 10.1109/isspit.2017.8388675
Category
Aktywność konferencyjna
Type
materiały konferencyjne indeksowane w Web of Science
Language
angielski
Publication year
2018

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