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Examining Feature Vector for Phoneme Recognition / Analiza parametrów w kontekście automatycznej klasyfikacji fonemów
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.
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- ISSPIT 2017, 17th IEEE International Symposium on Signal Processing and Information Technology strony 262 - 266
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Korvel G., Kostek B.: Examining Feature Vector for Phoneme Recognition / Analiza parametrów w kontekście automatycznej klasyfikacji fonemów// ISSPIT 2017, 17th IEEE International Symposium on Signal Processing and Information Technology/ : , 2017, s.262-266
- Verified by:
- Gdańsk University of Technology
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