Spanish Sign Language synthesis system

López-Colino, Fernando; Colás, José

doi:10.1016/j.jvlc.2012.01.003

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…Some approaches use voice recognition techniques to translate from spoken language into sign language, while other approaches translate written into sign language [12][13][14][15][16][17]. The authors in [11] note that voice recognition limits its action to specific domains and are not very efficient, at a rate of 8 s per sentence (an impractical solution for real time).…”

Section: Technologies Used In Sl Machine Translationmentioning

confidence: 99%

A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

et al. 2019

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Sign languages (SL) are the first language for most deaf people. Consequently, bidirectional communication among deaf and non-deaf people has always been a challenging issue. Sign language usage has increased due to inclusion policies and general public agreement, which must then become evident in information technologies, in the many facets that comprise sign language understanding and its computational treatment. In this study, we conduct a thorough systematic mapping of translation-enabling technologies for sign languages. This mapping has considered the most recommended guidelines for systematic reviews, i.e., those pertaining software engineering, since there is a need to account for interdisciplinary areas of accessibility, human computer interaction, natural language processing, and education, all of them part of ACM (Association for Computing Machinery) computing classification system directly related to software engineering. An ongoing development of a software tool called SYMPLE (SYstematic Mapping and Parallel Loading Engine) facilitated the querying and construction of a base set of candidate studies. A great diversity of topics has been studied over the last 25 years or so, but this systematic mapping allows for comfortable visualization of predominant areas, venues, top authors, and different measures of concentration and dispersion. The systematic review clearly shows a large number of classifications and subclassifications interspersed over time. This is an area of study in which there is much interest, with a basically steady level of scientific publications over the last decade, concentrated mainly in the European continent. The publications by country, nevertheless, usually favor their local sign language.

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Section: Technologies Used In Sl Machine Translationmentioning

confidence: 99%

A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

et al. 2019

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“…Chinese [4], Australian [5], Arabic [6,7], Indian [8], Spanish [9] and Japanese [10]. For more reviews on sign language and different approaches developed for SLR systems refer to [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Developing efficient SLR systems can facilitate the communication of deaf people in society and remove the barriers for them.One of the basic issues regarding SLR is that there is not a universal sign language.Sign languages of different countries have their own grammar rules. SLR systems have been rapidly developed in recent years for different sign languages including American [1-3], Chinese [4], Australian [5], Arabic [6,7], Indian [8], Spanish [9] and Japanese [10]. For more reviews on sign language and different approaches developed for SLR systems refer to [11,12].Due to its broad range of capabilities, Machine Vision (MV) is the major tool used in the development of SLR systems.…”

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confidence: 99%

Trajectory-based recognition of dynamic Persian sign language using hidden Markov model

Azar

Seyedarabi²

2020

Computer Speech & Language

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Sign Language Recognition (SLR) is an important step in facilitating the communication among deaf people and the rest of society. Existing Persian sign language recognition systems are mainly restricted to static signs which are not very useful in everyday communications.In this study, a dynamic Persian sign language recognition system is presented. A collection of 1200 videos were captured from 12 individuals performing 20 dynamic signs with a simple white glove. The trajectory of the hands, along with hand shape information were extracted from each video using a simple region-growing technique. These time-varying trajectories were then modeled using Hidden Markov Model (HMM) with Gaussian probability density functions as observations. The performance of the system was evaluated in different experimental strategies. Signer-independent and signer-dependent experiments were performed on the proposed system and the average accuracy of 97.48% was obtained. The experimental results demonstrated that the performance of the system is independent of the subject and it can also perform excellently even with a limited number of training data. are manual gestures, while non-manual gestures like head movements (e.g. nodding), body movement (e.g. shrugging) and face expressions also play an important role in sign communications. Unfortunately, the use of sign language is usually restricted to the deaf community resulting in restricted communication of them with the rest of the world. Therefore, they are usually excluded from society and deprived of their rights to have equal educational and career opportunities. In order to address this problem, Sign Language Recognition (SLR) systems are developed to translate this language into speech or text. Developing efficient SLR systems can facilitate the communication of deaf people in society and remove the barriers for them.One of the basic issues regarding SLR is that there is not a universal sign language.Sign languages of different countries have their own grammar rules. SLR systems have been rapidly developed in recent years for different sign languages including American [1-3], Chinese [4], Australian [5], Arabic [6,7], Indian [8], Spanish [9] and Japanese [10]. For more reviews on sign language and different approaches developed for SLR systems refer to [11,12].Due to its broad range of capabilities, Machine Vision (MV) is the major tool used in the development of SLR systems. An MV-based SLR system usually consists of three components: hand tracker, feature extractor and classifier. Hand trackers job is to segment hand regions from the background of the input video frames. Some studies rely on data gloves to track the hand movements for hand tracking [6,[13][14][15][16]. Although these gloves are

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