Speech production by a mechanical model: Construction of a vocal tract and its control by neural network

Higashimoto, T.; Sawada, Hideyuki

doi:10.1109/robot.2002.1014322

Cited by 21 publications

(17 citation statements)

References 3 publications

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“…There have been several attempts to build mechanical models of the vocal tract [21] [18]. While these can produce some human like sounds they are still rather limited and there are no commercially available mechanical solutions.…”

Section: Speech Production Unitmentioning

confidence: 99%

Multimodal Language Acquisition Based on Motor Learning and Interaction

Hörnstein¹,

Gustavsson

Santos-Victor³

et al. 2010

Studies in Computational Intelligence

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Section: Speech Production Unitmentioning

confidence: 99%

Multimodal Language Acquisition Based on Motor Learning and Interaction

Hörnstein¹,

Gustavsson

Santos-Victor³

et al. 2010

Studies in Computational Intelligence

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“…There has been several attempts to build mechanical models of the vocal tract [5] [6]. While these can produce some human like sounds they are still pretty limited and there are no commercially [7] where the vocal tract is considered to be a number of concatenated tubes with variable diameter.…”

Section: A Speech Production Unitmentioning

confidence: 99%

A unified approach to speech production and recognition based on articulatory motor representations

Hörnstein¹,

Santos-Victor²

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present a unified approach for speech production and recognition based on articulatory motor representations. The approach is inspired by the Motor theory and the discovery of Mirror neurons, and use motor representations for both reproduction and recognition of speech. A model of the vocal tract is used to create sound and the created sound is then mapped back to the motor representation using a neural network. To learn the map we mimic the behavior of a child that uses a combination of babbling and interaction with its caregiver to learn how to speak. Several different phases of babbling and interaction are identified and described. These help to overcome the inversion problem. The approach has been implemented on a humanoid robot, which has successfully learned to pronounce Swedish and Portuguese vowels. We have also studied how the different phases of babbling and interaction effect the error of the map and the achieved recognition rate when presented with vowels from different subjects. Finally we compare the recognition rates obtained using motor space with recognition rates obtained by directly using the acoustic parameters.

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“…This is often referred to as the source-filter theory of speech production [12] and has been implemented in previous studies [7], [6]. To model the process of vowel convergence in mother-infant interaction, we improved the vocal robot used in a previous study [7] such that the sound source was replaced by an air .…”

Section: A Vocal Robotmentioning

confidence: 99%

“…[5], [6]) is one approach to directly attack the problem of imitation between dissimilar bodies. Using such a vocal robot, Yoshikawa et al [7] proposed a mother-infant interaction model for infant vowel acquisition based on observations in developmental psychology.…”

Section: Introductionmentioning

confidence: 99%

Finding the Correspondence of Caregiver's Vowel Categories Based on Unconscious Anchoring in Maternal Imitation

Miura

Yoshikawa²,

Asada

2007

RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication

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Abstract-Due to differences in body structure between robots and humans, it is a formidable task for robots to show behaviors that correspond to human behaviors. As a simple case of this correspondence problem, this paper presents a robot that learns to vocalize vowels through interaction with its caregiver. Inspired by the findings in developmental psychology, we focus on the role of maternal imitation (i.e., imitation of a robot voice by a caregiver), which could play a role in guiding the correspondence of sounds. Furthermore, we suppose that it causes unconscious anchoring in which the imitated voice by the caregiver is approaching to one of his/her own vowels without his/her intension, and thereby works for guiding robot's utterances to be more vowel-like. We propose a method for vowel learning with an imitative caregiver under the assumption that the robot knows the desired categories of caregiver's vowels and the rough estimate of mapping between the region of sounds that the caregiver can generate and the region that the robot can generate. Through experiments with a Japanese imitative caregiver, we show that a robot succeeds in acquiring more vowel-like utterances than would be possible without such a caregiver, even when the robot is provided different mapping functions.

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Speech production by a mechanical model: Construction of a vocal tract and its control by neural network

Cited by 21 publications

References 3 publications

Multimodal Language Acquisition Based on Motor Learning and Interaction

Multimodal Language Acquisition Based on Motor Learning and Interaction

A unified approach to speech production and recognition based on articulatory motor representations

Finding the Correspondence of Caregiver's Vowel Categories Based on Unconscious Anchoring in Maternal Imitation

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