Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting" - Publikacja - MOST Wiedzy

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Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting"

Abstrakt

The purpose of this study is to involve both Convolutional Neural Networks and a typical learning algorithm in the allophone classification process. A list of words including aspirated and non-aspirated allophones pronounced by native and non-native English speakers is recorded and then edited and analyzed. Allophones extracted from English speakers’ recordings are presented in the form of two-dimensional spectrogram images and used as input to train the Convolutional Neural Networks. Various settings of the spectral representation are analyzed to determine adequate option for the allophone classification. Then, testing is performed on the basis of non-native speakers’ utterances. The same approach is repeated employing learning algorithm but based on feature vectors. The achieved classification results are promising as high accuracy is observed.

Informacje szczegółowe

Kategoria:
Aktywność konferencyjna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Tytuł wydania:
145 Audio Engineering Society Convention strony 1 - 7
Język:
angielski
Rok wydania:
2018
Opis bibliograficzny:
Piotrowska M., Korvel G., Kurowski A., Kostek B., Czyżewski A.: Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting"// 145 Audio Engineering Society Convention/ New York: Audio Engineering Society, 2018, s.1-7
Weryfikacja:
Politechnika Gdańska

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