Speaker identification and clustering using convolutional neural networks

Lukic, Yanick; Vogt, Carlo; Dürr, Oliver; Stadelmann, Thilo

doi:10.21256/zhaw-3761

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3761

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Speaker identification and clustering using convolutional neural networks
Authors:	Lukic, Yanick Vogt, Carlo Dürr, Oliver Stadelmann, Thilo
DOI:	10.21256/zhaw-3761 10.1109/MLSP.2016.7738816
Proceedings:	2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),
Conference details:	26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietri sul Mare, Italy, 13-16 Sept. 2016
Issue Date:	2016
Publisher / Ed. Institution:	IEEE
ISBN:	978-1-5090-0746-2
Other identifiers:	INSPEC Accession Number: 16449884
Language:	English
Subjects:	Datalab; Speaker identification; Speaker clustering; Deep learning
Subject (DDC):	006: Special computer methods
Abstract:	Deep learning, especially in the form of convolutional neural networks (CNNs), has triggered substantial improvements in computer vision and related fields in recent years. This progress is attributed to the shift from designing features and subsequent individual sub-systems towards learning features and recognition systems end to end from nearly unprocessed data. For speaker clustering, however, it is still common to use handcrafted processing chains such as MFCC features and GMM-based models. In this paper, we use simple spectrograms as input to a CNN and study the optimal design of those networks for speaker identification and clustering. Furthermore, we elaborate on the question how to transfer a network, trained for speaker identification, to speaker clustering. We demonstrate our approach on the well known TIMIT dataset, achieving results comparable with the state of the art – without the need for handcrafted features.
URI:	https://digitalcollection.zhaw.ch/handle/11475/7087
Fulltext version:	Accepted version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Information Technology (InIT) Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

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Lukic, Y., Vogt, C., Dürr, O., & Stadelmann, T. (2016). Speaker identification and clustering using convolutional neural networks. 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),. https://doi.org/10.21256/zhaw-3761

Lukic, Y. et al. (2016) ‘Speaker identification and clustering using convolutional neural networks’, in 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),. IEEE. Available at: https://doi.org/10.21256/zhaw-3761.

Y. Lukic, C. Vogt, O. Dürr, and T. Stadelmann, “Speaker identification and clustering using convolutional neural networks,” in 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016. doi: 10.21256/zhaw-3761.

LUKIC, Yanick, Carlo VOGT, Oliver DÜRR und Thilo STADELMANN, 2016. Speaker identification and clustering using convolutional neural networks. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),. Conference paper. IEEE. 2016. ISBN 978-1-5090-0746-2

Lukic, Yanick, Carlo Vogt, Oliver Dürr, and Thilo Stadelmann. 2016. “Speaker Identification and Clustering Using Convolutional Neural Networks.” Conference paper. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),. IEEE. https://doi.org/10.21256/zhaw-3761.

Lukic, Yanick, et al. “Speaker Identification and Clustering Using Convolutional Neural Networks.” 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 2016, https://doi.org/10.21256/zhaw-3761.