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
Authors
- Grazina Korvel,
- prof. dr hab. inż. Bożena Kostek link open in new tab
Additional information
- 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
Source: MOSTWiedzy.pl - publication "Examining Feature Vector for Phoneme Recognition" link open in new tab