Assessment of Features for Neurocomputational Modeling of Speech Acquisition

Shitov, D, Pirogova, E and Lech, M 2018, 'Assessment of Features for Neurocomputational Modeling of Speech Acquisition', in 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS), Cairns, Australia, Australia, 17-19 December 2018, pp. 1-6.


Document type: Conference Paper
Collection: Conference Papers

Title Assessment of Features for Neurocomputational Modeling of Speech Acquisition
Author(s) Shitov, D
Pirogova, E
Lech, M
Year 2018
Conference name 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)
Conference location Cairns, Australia, Australia
Conference dates 17-19 December 2018
Proceedings title 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)
Publisher IEEE
Place of publication USA
Start page 1
End page 6
Total pages 6
Abstract The aim of this study is to determine the most suitable speech representation (features) for the neurocomputational modeling of the speech acquisition process. Majority of the existing techniques apply the mel frequency cepstral coefficients (MFCCs). Recent advancements in deep learning technologies created an opportunity for using a deep network parameters to represent speech signals. In this study, two experiments were conducted to obtain both qualitative and quantitative assessments of the modeling suitability of four different types of features: formants, MFCCs, MFCCs-PCA and neural network features. The results show that features extracted from the modified Convolutional Neural Network with a Long Short-Term Memory layer (CNN-LSTM) clearly outperformed all other types of features.
Subjects Signal Processing
Keyword(s) speech acquisition
speech features
speech classification
neural networks
DOI - identifier 10.1109/ICSPCS.2018.8631770
Copyright notice © 2018 IEEE
ISBN 9781538656020
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