Tongue Contour Reconstruction from Optical and Electrical Palatography

Mumtaz, R; Preuß, Simon; Neuschaefer‐Rube, Christiane; Hey, Christiane; Sader, Robert; Birkholz, Peter

doi:10.1109/lsp.2014.2312456

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…This research was extended in [255], [256] to enable the recognition of German command words. In [257], EOS data were used to reconstruct the tongue contour using a multiple linear regression model. A problem with OPG is that an error is introduced if the tongue is not oriented perpendicular to the axes of the optical sensors.…”

Section: ) Palatographymentioning

confidence: 99%

Silent Speech Interfaces for Speech Restoration: A Review

et al. 2020

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This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.

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Section: ) Palatographymentioning

confidence: 99%

Silent Speech Interfaces for Speech Restoration: A Review

et al. 2020

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“…Displaying the place of articulation can effectively help a subject to imitate articulation, and is thus suitable feedback for articulation learning systems. Numerous previous studies have focused on 3D models of the tongue, which are generated from electromagnetic articulography, ultrasound, electropalatography, cineradiography, X-ray microbeam, and magnetic resonance imaging (Liu et al, 2012;Westbury, 1994;Kramer et al, 1991;Munhall et al, 1995;Yang et al, 2013;Mumtaz et al, 2014). Tongue movement can be estimated and used to generate 3D models of tongue and lip animations.…”

Section: Introductionmentioning

confidence: 99%

A computer-aided articulation learning system for subjects with articulation disorders

Chen

Wu²

2016

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Purpose Articulation errors substantially reduce speech intelligibility and the ease of spoken communication. Moreover, the articulation learning process that speech-language pathologists must provide is time consuming and expensive. The purpose of this paper, to facilitate the articulation learning process, is to develop a computer-aided articulation learning system to help subjects with articulation disorders. Design/methodology/approach Facial animations, including lip and tongue animations, are used to convey the manner and place of articulation to the subject. This process improves the effectiveness of articulation learning. An interactive learning system is implemented through pronunciation confusion networks (PCNs) and automatic speech recognition (ASR), which are applied to identify mispronunciations. Findings Speech and facial animations are effective for assisting subjects in imitating sounds and developing articulatory ability. PCNs and ASR can be used to automatically identify mispronunciations. Research limitations/implications Future research will evaluate the clinical performance of this approach to articulation learning. Practical implications The experimental results of this study indicate that it is feasible for clinically implementing a computer-aided articulation learning system in learning articulation. Originality/value This study developed a computer-aided articulation learning system to facilitate improving speech production ability in subjects with articulation disorders.

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