期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2019
卷号：29
期号：2
页码：1-13
DOI：10.2478/amcs-2019-0029
出版社：De Gruyter Open
摘要：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.
