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MACHINE LEARNING–BASED ANALYSIS OF ENGLISH LATERAL ALLOPHONES

Abstract

Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and selforganizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.

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Category:
Articles
Type:
artykuły w czasopismach
Published in:
International Journal of Applied Mathematics and Computer Science no. 29, pages 393 - 405,
ISSN: 1641-876X
Language:
English
Publication year:
2019
Bibliographic description:
Piotrowska M., Korvel G., Kostek B., Ciszewski T., Czyżewski A.: MACHINE LEARNING–BASED ANALYSIS OF ENGLISH LATERAL ALLOPHONES// International Journal of Applied Mathematics and Computer Science -Vol. 29,iss. 2 (2019), s.393-405
DOI:
Digital Object Identifier (open in new tab) 10.2478/amcs-2019-0029
Sources of funding:
Verified by:
Gdańsk University of Technology

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