Evidence map›Paper›PMID 36433196›Full record

ArticleSensors (Basel, Switzerland)2022

Optimizing the Ultrasound Tongue Image Representation for Residual Network-Based Articulatory-to-Acoustic Mapping.

Tamás Gábor Csapó, Gábor Gosztolya, László Tóth, Amin Honarmandi Shandiz, Alexandra Markó

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Tamás Gábor CsapóDepartment of Telecommunications and Media Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Hungary.ORCID 0000-0003-4375-7524
Gábor GosztolyaELRN-SZTE Research Group on Artificial Intelligence, H-6720 Szeged, Hungary.ORCID 0000-0002-2864-6466
László TóthInstitute of Informatics, University of Szeged, H-6720 Szeged, Hungary.
Amin Honarmandi ShandizInstitute of Informatics, University of Szeged, H-6720 Szeged, Hungary.ORCID 0000-0002-8561-7629
Alexandra MarkóMTA-ELTE Lendület Lingual Articulation Research Group, H-1088 Budapest, Hungary.ORCID 0000-0003-0301-7134

Funding

Artificial Intelligence National Laboratory RRF-2.3.1-21-2022-00004European Commission NAHungarian Academy of Sciences NAHungarian Ministry of Innovation and Technology NRDI Office TKP2021-NVA-09Ministry for Culture and Innovation ÚNKP-22-5-BME-316National Research, Development and Innovation Office of Hungary FK 142163
6 · The paper itself

Abstract

Within speech processing, articulatory-to-acoustic mapping (AAM) methods can apply ultrasound tongue imaging (UTI) as an input. (Micro)convex transducers are mostly used, which provide a wedge-shape visual image. However, this process is optimized for the visual inspection of the human eye, and the signal is often post-processed by the equipment. With newer ultrasound equipment, now it is possible to gain access to the raw scanline data (i.e., ultrasound echo return) without any internal post-processing. In this study, we compared the raw scanline representation with the wedge-shaped processed UTI as the input for the residual network applied for AAM, and we also investigated the optimal size of the input image. We found no significant differences between the performance attained using the raw data and the wedge-shaped image extrapolated from it. We found the optimal pixel size to be 64 × 43 in the case of the raw scanline input, and 64 × 64 when transformed to a wedge. Therefore, it is not necessary to use the full original 64 × 842 pixels raw scanline, but a smaller image is enough. This allows for the building of smaller networks, and will be beneficial for the development of session and speaker-independent methods for practical applications. AAM systems have the target application of a "silent speech interface", which could be helpful for the communication of the speaking-impaired, in military applications, or in extremely noisy conditions.

Indexed as

AcousticsTongueHumansNoiseSpeechUltrasonographydeep learningspeech processingultrasound imaging

Identifiers

PMID36433196
PMCPMC9696288

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.