Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies

University of Sheffield Heidi Christensen, Florida Institute for Human Kristy Hollingshead, Machine Cognition, Boston College Emily Prud’hommeaux, University of Toronto Frank Rudzicz, Michigan Technological University Keith Vertanen (Editors)


Anthology ID:
W19-17
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | SLPAT | WS
SIG:
SIGSLPAT
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W19-17
DOI:
Bib Export formats:
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PDF:
https://aclanthology.org/W19-17.pdf

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Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies
University of Sheffield Heidi Christensen | Florida Institute for Human Kristy Hollingshead | Machine Cognition | Boston College Emily Prud’hommeaux | University of Toronto Frank Rudzicz | Michigan Technological University Keith Vertanen

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Permanent Magnetic Articulograph (PMA) vs Electromagnetic Articulograph (EMA) in Articulation-to-Speech Synthesis for Silent Speech InterfacePMA) vs Electromagnetic Articulograph (EMA) in Articulation-to-Speech Synthesis for Silent Speech Interface
Beiming Cao | Nordine Sebkhi | Ted Mau | Omer T. Inan | Jun Wang

Silent speech interfaces (SSIs) are devices that enable speech communication when audible speech is unavailable. Articulation-to-speech (ATS) synthesis is a software design in SSI that directly converts articulatory movement information into audible speech signals. Permanent magnetic articulograph (PMA) is a wireless articulator motion tracking technology that is similar to commercial, wired Electromagnetic Articulograph (EMA). PMA has shown great potential for practical SSI applications, because it is wireless. The ATS performance of PMA, however, is unknown when compared with current EMA. In this study, we compared the performance of ATS using a PMA we recently developed and a commercially available EMA (NDI Wave system). Datasets with same stimuli and size that were collected from tongue tip were used in the comparison. The experimental results indicated the performance of PMA was close to, although not as equally good as that of EMA. Furthermore, in PMA, converting the raw magnetic signals to positional signals did not significantly affect the performance of ATS, which support the future direction in PMA-based ATS can be focused on the use of positional signals to maximize the benefit of spatial analysis.

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Investigating Speech Recognition for Improving Predictive AACAAC
Jiban Adhikary | Robbie Watling | Crystal Fletcher | Alex Stanage | Keith Vertanen

Making good letter or word predictions can help accelerate the communication of users of high-tech AAC devices. This is particularly important for real-time person-to-person conversations. We investigate whether per forming speech recognition on the speaking-side of a conversation can improve language model based predictions. We compare the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines (Google and IBM Watson). We found that despite recognition word error rates of 7-16 %, our ensemble of N-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts.