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Abstract
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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Authors (2)
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Grazina Korvel
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Keywords
Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- 17th IEEE International Symposium on Signal Processing and Information Technology(ISSPIT 2017) strony 384 - 398
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Korvel G., Kostek B..: Examining Feature Vector for Phoneme Recognition, W: 17th IEEE International Symposium on Signal Processing and Information Technology(ISSPIT 2017), 2018, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/isspit.2017.8388675
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
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