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Open AccessArticle

An Effective and Efficient Genetic-Fuzzy Algorithm for Supporting Advanced Human-Machine Interfaces in Big Data Settings

by Alfredo Cuzzocrea 1,*,†, Enzo Mumolo 2,† and Giorgio Mario Grasso 3,†
1
iDEA Lab, University of Calabria, 87036 Rende, Italy
2
DIA Department, University of Trieste, 34127 Trieste, Italy
3
COSPECS Department, University of Messina, 98121 Messina, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Algorithms 2020, 13(1), 13; https://doi.org/10.3390/a13010013
Received: 21 November 2019 / Revised: 18 December 2019 / Accepted: 18 December 2019 / Published: 31 December 2019
In this paper we describe a novel algorithm, inspired by the mirror neuron discovery, to support automatic learning oriented to advanced man-machine interfaces. The algorithm introduces several points of innovation, based on complex metrics of similarity that involve different characteristics of the entire learning process. In more detail, the proposed approach deals with an humanoid robot algorithm suited for automatic vocalization acquisition from a human tutor. The learned vocalization can be used to multi-modal reproduction of speech, as the articulatory and acoustic parameters that compose the vocalization database can be used to synthesize unrestricted speech utterances and reproduce the articulatory and facial movements of the humanoid talking face automatically synchronized. The algorithm uses fuzzy articulatory rules, which describe transitions between phonemes derived from the International Phonetic Alphabet (IPA), to allow simpler adaptation to different languages, and genetic optimization of the membership degrees. Large experimental evaluation and analysis of the proposed algorithm on synthetic and real data sets confirms the benefits of our proposal. Indeed, experimental results show that the vocalization acquired respects the basic phonetic rules of Italian languages and that subjective results show the effectiveness of multi-modal speech production with automatic synchronization between facial movements and speech emissions. The algorithm has been applied to a virtual speaking face but it may also be used in mechanical vocalization systems as well. View Full-Text
Keywords: genetic optimization; fuzzy algorithms; advanced human-machine interfaces; humanoid robotics genetic optimization; fuzzy algorithms; advanced human-machine interfaces; humanoid robotics
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Cuzzocrea, A.; Mumolo, E.; Grasso, G.M. An Effective and Efficient Genetic-Fuzzy Algorithm for Supporting Advanced Human-Machine Interfaces in Big Data Settings. Algorithms 2020, 13, 13.

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