- Tytuł:
- Machine learning-based analysis of English lateral allophones
- Autorzy:
-
Piotrowska, Magdalena
Korvel, Gražina
Kostek, Bożena
Ciszewski, Tomasz
Czyżewski, Andrzej - Powiązania:
- https://bibliotekanauki.pl/articles/908115.pdf
- Data publikacji:
- 2019
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
allophone
audio features
artificial neural network
k-nearest neighbor
self organizing map
alofon
cechy akustyczne
sztuczna sieć neuronowa
metoda najbliższych sąsiadów
mapa samoorganizująca - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 393-405
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł