Three dimensional tongue with liquid sealing mechanism for improving resonance on an anthropomorphic talking robot

Fukui, Kotaro; Ishikawa, Yuma; Ohno, Keisuke; Sakakibara, Nana; Honda, Masaaki; Takanishi, Atsuo

doi:10.1109/iros.2009.5353983

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…Because their main purpose was to investigate animatronic control, the quality of the reproduced sounds was not discussed. The most recent version of Waseda Talker is WT-7R [67], which offers improved tongue performance, resulting in clearer vowels and a sharper bandwidth for the formant peak in the spectral data.…”

Section: E Research Platformsmentioning

confidence: 99%

Modeling Early Vocal Development Through Infant–Caregiver Interaction: A Review

Asada

2016

IEEE Trans. Cogn. Dev. Syst.

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The developmental origin of language communication seems to involve vocal interactions between an infant and a caregiver, and one of the big mysteries related to this is how an infant learns to vocalize the caregiver's native language. Many theories attempt to explain this ability of infant as imitation based on acoustic matching. However, the acoustic qualities of speech produced by the infant and caregiver are quite different and therefore cannot be fully explained by imitation. Instead, the interaction itself may have an important role to play, but the mechanism is still unclear. In this article, we review studies addressing this topic based on explicit interaction mechanisms using computer simulations and/or real vocal robots. The relationships between these approaches are analyzed after a brief review of the early development of an infant's speech perception and articulation based on observational studies in developmental psychology and a few neuroscientific imaging studies. Finally, future issues related to real infant-caregiver vocal interaction are outlined.

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Section: E Research Platformsmentioning

confidence: 99%

Modeling Early Vocal Development Through Infant–Caregiver Interaction: A Review

Asada

2016

IEEE Trans. Cogn. Dev. Syst.

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“…Given an articulatory synthesizer, either mechanical [10,31] or physically simulated [15], articulatory synthesis takes a time series of articulatory parameters to produce speech signal. Several methods [13,3] have been proposed to infer proper articulatory parameters to reproduce a given speech by building parameter-sound pair datasets first and then training a model with the datasets.…”

Section: Acoustic-to-articulatory Inversion (Aai)mentioning

confidence: 99%

Embodied Self-supervised Learning by Coordinated Sampling and Training

Sun

2020

Preprint

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Self-supervised learning can significantly improve the performance of downstream tasks, however, the dimensions of learned representations normally lack explicit physical meanings. In this work, we propose a novel self-supervised approach to solve inverse problems by employing the corresponding physical forward process so that the learned representations can have explicit physical meanings. The proposed approach works in an analysis-by-synthesis manner to learn an inference network by iteratively sampling and training. At the sampling step, given observed data, the inference network is used to approximate the intractable posterior, from which we sample input parameters and feed them to a physical process to generate data in the observational space; At the training step, the same network is optimized with the sampled paired data. We prove the feasibility of the proposed method by tackling the acoustic-to-articulatory inversion problem to infer articulatory information from speech. Given an articulatory synthesizer, an inference model can be trained completely from scratch with random initialization. Our experiments demonstrate that the proposed method can converge steadily and the network learns to control the articulatory synthesizer to speak like a human. We also demonstrate that trained models can generalize well to unseen speakers or even new languages, and performance can be further improved through self-adaptation.• Non-differentiable physical process. The physical process may be non-differentiable w.r.t. its input, thus auto-encoder like methods cannot be directly applied.Preprint. Under review.

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“…Talking robots have long been under development at Waseda university in Tokyo. The latest prototype was able to control the movement of a 510 rubber tongue with seven degrees of freedom, three for the tip, two for the blade and the remaining two in the body (Fukui et al, 2009). Another mechanical device, inspired by the motion of the tongue, and actuated through tendons was developed by Kawamura and co-workers.…”

Section: In Vitro and In Silico Models Of The Oral Phase Of Swallowingmentioning

confidence: 99%