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 algorithmintroduces several points of innovation, based on complex metrics of similarity that involve differentcharacteristics of the entire learning process. In more detail, the proposed approach deals withan 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 andacoustic parameters that compose the vocalization database can be used to synthesize unrestrictedspeech utterances and reproduce the articulatory and facial movements of the humanoid talkingface automatically synchronized. The algorithm uses fuzzy articulatory rules, which describetransitions between phonemes derived from the International Phonetic Alphabet (IPA), to allowsimpler adaptation to different languages, and genetic optimization of the membership degrees.Large experimental evaluation and analysis of the proposed algorithm on synthetic and real datasets confirms the benefits of our proposal. Indeed, experimental results show that the vocalizationacquired respects the basic phonetic rules of Italian languages and that subjective results showthe effectiveness of multi-modal speech production with automatic synchronization between facialmovements and speech emissions. The algorithm has been applied to a virtual speaking face but itmay also be used in mechanical vocalization systems as well.

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

Cuzzocrea, Alfredo
;
2020-01-01

Abstract

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 algorithmintroduces several points of innovation, based on complex metrics of similarity that involve differentcharacteristics of the entire learning process. In more detail, the proposed approach deals withan 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 andacoustic parameters that compose the vocalization database can be used to synthesize unrestrictedspeech utterances and reproduce the articulatory and facial movements of the humanoid talkingface automatically synchronized. The algorithm uses fuzzy articulatory rules, which describetransitions between phonemes derived from the International Phonetic Alphabet (IPA), to allowsimpler adaptation to different languages, and genetic optimization of the membership degrees.Large experimental evaluation and analysis of the proposed algorithm on synthetic and real datasets confirms the benefits of our proposal. Indeed, experimental results show that the vocalizationacquired respects the basic phonetic rules of Italian languages and that subjective results showthe effectiveness of multi-modal speech production with automatic synchronization between facialmovements and speech emissions. The algorithm has been applied to a virtual speaking face but itmay also be used in mechanical vocalization systems as well.
2020
genetic optimization
fuzzy algorithms
advanced human-machine interfaces
humanoid robotics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/312487
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