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Paper details
Number 2 - June 2019
Volume 29 - 2019
Machine learning-based analysis of English lateral allophones
Magdalena Piotrowska, Gražina Korvel, Bożena Kostek, Tomasz Ciszewski, Andrzej Czyżewski
Abstract
Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing 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.
Keywords
allophones, audio features, artificial neural networks (ANNs), k-nearest neighbor (kNN), self-organizing map (SOM)