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dc.contributor.authorHoward, Ian

Here we extend previous work for the estimation of the time of excitation (Tx) from the speech signal using a shallow neural network. We make use of a dataset that consists of the simultaneously recorded speech and Laryngograph signals from drama students speaking a phonetically balanced passage. We first use the Laryngograph signal to estimate the location of vocal fold closures as a function of time. Then, by considering the problem as a supervised learning task, we train a multilayer perceptron to map between raw speech samples, selected using a sliding input window, to a single output target sample that represents the presence or absence of an excitation point. We present result of operation across several male speakers and also demonstrate that it is possible to reconstruct the Laryngograph directly from the speech signal.

dc.titleSpeech Fundamental Period Estimation using a Neural Network
plymouth.conference-nameESSV 2020 Magdeburg
plymouth.journalStudientexte zur Sprachkommunikation Band 95: Elektronische Sprachsignalverarbeitung 2020 Conference proceedings of the 31st conference in Magdeburg with 38 contributions. ISBN: 978-3-959081-93-1
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering|School of Engineering, Computing and Mathematics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA11 Computer Science and Informatics

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