Title:	Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech
Authors:	Matoušek, Jindřich Tihelka, Daniel
Citation:	MATOUŠEK, J. TIHELKA, D. Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech. In Artificial Neural Networks in Pattern Recognition; 10th IAPR TC3 Workshop, ANNPR 2022; Dubai, United Arab Emirates, November 24-26, 2022; Proceedings. Cham: Springer Nature Switzerland AG, 2022. s. 107-120. ISBN: 978-3-031-20649-8 , ISSN: 0302-9743
Issue Date:	2022
Publisher:	Springer Nature Switzerland AG
Document type:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85142752874 http://hdl.handle.net/11025/51298
ISBN:	978-3-031-20649-8
ISSN:	0302-9743
Keywords in different language:	glottal closure instant detection;deep learning;recurrent neural network;convolutional neural network
Abstract in different language:	In this paper, we propose to frame glottal closure instant (GCI) de- tection from raw speech as a sequence-to-sequence prediction problem and to explore the potential of recurrent neural networks (RNNs) to handle this prob- lem. We compare the RNN architecture to widely used convolutional neural net- works (CNNs) and to some other machine learning-based and traditional non- learning algorithms on several publicly available databases. We show that the RNN architecture improves GCI detection. The best results were achieved for a joint CNN-BiLSTM model in which RNN is composed of bidirectional long short-term memory (BiLSTM) units and CNN layers are used to extract relevant features.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © The Author(s), under exclusive licence to Springer Nature B.V.
Appears in Collections:	Konferenční příspěvky / Conference Papers (KKY) OBD

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