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Open AccessFeature PaperArticle

Semi-Supervised Deep Time-Delay Embedded Clustering for Stress Speech Analysis

1
Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
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Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1263; https://doi.org/10.3390/electronics8111263
Received: 23 September 2019 / Revised: 25 October 2019 / Accepted: 28 October 2019 / Published: 1 November 2019
(This article belongs to the Section Artificial Intelligence)
Real stressed speech is affected by various aspects (individual characteristics and environment) so that the stress patterns are diverse and different on each individual. To this end, in our previous work, we performed an unsupervised clustering method that able to self-learning manner by mapping the feature representations of the stress speech and clustering tasks simultaneously, called deep time-delay embedded clustering (DTEC). However, DTEC has not confirmed yet the compatibility between the output class and informational classes. Therefore, we proposed semi-supervised time-delay embedded clustering (SDTEC) as a new framework of semi-supervised in DTEC. SDTEC incorporates the prior information of pairwise constraints in the embedding layer and simultaneously learns the feature representation and the clustering assignments. The prior information was used to guide the clustering procedure so that the points that belong to the incorrect cluster can be corrected. The effectiveness of the proposed SDTEC was evaluated by comparing it with some baseline methods in terms of the clustering error rate (CER). Moreover, to demonstrate SDTEC’s capabilities, we conducted a comprehensive ablation study. Based on experiment results, SDTEC outperformed the baseline methods and achieves state-of-the-art results in semi-supervised clustering. View Full-Text
Keywords: semi-supervised; clustering; stress speech; deep clustering; DNN; TDNN; prior knowledge; pairwise constraints semi-supervised; clustering; stress speech; deep clustering; DNN; TDNN; prior knowledge; pairwise constraints
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MDPI and ACS Style

Prasetio, B.H.; Tamura, H.; Tanno, K. Semi-Supervised Deep Time-Delay Embedded Clustering for Stress Speech Analysis. Electronics 2019, 8, 1263.

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