Modeling and Analysis of Elastic Tongue Mechanism of Talking Robot for Acoustic Simulation

Nishikawa, K.; Ogawara, Takayuki; Takanobu, Hideaki; Mochida, Takemi; Honda, Masaaki; Takanishi, Atsuo

doi:10.1299/kikaic.71.1892

Cited by 4 publications

(8 citation statements)

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“…As for voice synthesis, it is worth noting that in Humanoid robotics is very important, if not necessary, to produce speech using a mechanical speaking head for the reason we describe briefly. The motivation of the mechanical development of the speaking robot, as described in Reference [23], may be to conduct research on the vocal motor brain control system and to create a dynamic mechanical model for reproducing human language. In addition to telecommunications applications, medical training devices and learning devices mentioned in Reference [23], we believe that a mechanical head derived from articulate features can ultimately lead to a robotic mechanical face with natural behavior.…”

Section: Motivationsmentioning

confidence: 99%

“…The motivation of the mechanical development of the speaking robot, as described in Reference [23], may be to conduct research on the vocal motor brain control system and to create a dynamic mechanical model for reproducing human language. In addition to telecommunications applications, medical training devices and learning devices mentioned in Reference [23], we believe that a mechanical head derived from articulate features can ultimately lead to a robotic mechanical face with natural behavior. It is known, in fact, that there is a very high correlation between the dynamic of the vocal tract and the behavior of the facial movement, as emphasized by Yehia et al in Reference [24].…”

Section: Motivationsmentioning

confidence: 99%

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

2019

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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.

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Section: Motivationsmentioning

confidence: 99%

Section: Motivationsmentioning

confidence: 99%

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

2019

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“…A mechanical talking head has many applications in telecommunications, medical training devices and learning devices, as mentioned in Ref. 5. An important step toward a mechanical talking head development is to estimate accurate vocal tract dynamic parameters during phonation.…”

Section: Introductionmentioning

confidence: 99%

Towards Articulatory Control of Talking Heads in Humanoid Robotics Using a Genetic-Fuzzy Imitation Learning Algorithm

Mumolo

Nolich

2007

Int. J. Human. Robot.

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In human heads there is a strong structural linkage between the vocal tract and facial behavior during speech. For a robotic talking head to have human-like behavior, this linkage should be emulated. One way to do that is to estimate the articulatory features from a given utterance and to use them to control a talking head. In this paper, we describe an algorithm to estimate the articulatory features from a spoken sentence using a novel computational model of human vocalization. Our model uses a set of fuzzy rules and genetic optimization. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the values of the articulatory features. The fuzzy rules represent the relationships between places of articulation and speech acoustic parameters, and the genetic algorithm estimates the degrees of membership of the places of articulation according to an optimization criteria and it performs imitation learning. We verify our model by performing audio-visual subjective tests of animated talking heads showing that the algorithm is able to produce correct results. In particular, subjective listening tests of artificially generated sentences from the articulatory description resulted in an average phonetic accuracy slightly under 80%. Through the analysis of large amounts of natural speech, the algorithm can be used to learn the places of articulation of all phonemes of a given speaker. The estimated places of articulation are then used to control talking heads in humanoid robotics

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“…The motivation of mechanical talking robot development, as described in [5], can be to conduct research into the speech motor control system in the brain and to create a dynamic mechanical model to reproduce human speech. Besides the applications in telecommunications, medical training devices and learning devices mentioned in [5] we think that a mechanical talking head derived from articulatory features would eventually lead to a mechanical robotic face with natural behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the applications in telecommunications, medical training devices and learning devices mentioned in [5] we think that a mechanical talking head derived from articulatory features would eventually lead to a mechanical robotic face with natural behavior. It is known, in fact, that there is a very high correlation between the vocal tract dynamic and the facial motion behavior, as pointed out by Yehia et al in [6].…”

Section: Introductionmentioning

confidence: 99%

A genetic-fuzzy algorithm for the articulatory imitation of facial movements during vocalization of a humanoid robot

Mumolo

Nolich

Menegatti

5th IEEE-RAS International Conference on Humanoid Robots, 2005.

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Abstract-In human heads there is a strong structural linkage between vocal tract and facial behavior during speech. For a robotic talking head to have a human-like behavior, this linkage should be emulated. One way to do that is to compute an estimate of the articulatory features which produce a given utterance and then to transform them into facial animation. We present a computational model of human vocalization which is aimed at describing the articulatory mechanisms which produce spoken phonemes. It uses a set of fuzzy rules and genetic optimization. The former represents the relationships between places of articulations and speech acoustic parameters, while the latter estimates the degrees of membership of the places of articulation. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the articulatory features. The trajectories of articulatory parameters can be used to control a graphical or mechanical talking head. We verify the model presented here by generating and listening to artificial sentences. Subjective listening tests of artificially generated sentences from the articulatory description resulted in an average phonetic accuracy of about 79 %. Through the analysis of a large amount of natural speech, the algorithm can be used to learn the places of articulation of all phonemes of a given speaker.

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Modeling and Analysis of Elastic Tongue Mechanism of Talking Robot for Acoustic Simulation

Cited by 4 publications

References 0 publications

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

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

Towards Articulatory Control of Talking Heads in Humanoid Robotics Using a Genetic-Fuzzy Imitation Learning Algorithm

A genetic-fuzzy algorithm for the articulatory imitation of facial movements during vocalization of a humanoid robot

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