Assistive technologies for severe and profound hearing loss: Beyond hearing aids and implants

Hermawati, Setia; Pieri, Katerina

doi:10.1080/10400435.2018.1522524

Cited by 13 publications

(3 citation statements)

References 67 publications

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“…Similarly, McKee et al highlighted that the deaf community views deafness as a cultural identity, rather than a disability [41]. On this note, Hermawati and Pieri suggested that studying the acceptance and development of assistive technologies should focus on the overall welfare of DHH populations beyond only hearing-related challenges [20], such as social and cultural norms in DHH online communities.…”

Section: Accessibility Of Information and Communication Technologies ...mentioning

confidence: 99%

Sparkling Silence: Practices and Challenges of Livestreaming Among Deaf or Hard of Hearing Streamers

Cao,

He,

Zhou

et al. 2023

Preprint

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Understanding livestream platforms' accessibility challenges for minority groups, such as people with disabilities, is critical to increasing the diversity and inclusion of those platforms. While prior work investigated the experiences of streamers with vision or motor loss, little is known about the experiences of deaf or hard of hearing (DHH) streamers who must work with livestreaming platforms that heavily depend on audio. We conducted semi-structured interviews with DHH streamers to learn why they livestream, how they navigate livestream platforms and related challenges. Our findings revealed their desire to break the stereotypes towards the DHH groups via livestream and the intense interplay between interaction methods, such as sign language, texts, lip language, background music, and viewer characteristics. Major accessibility challenges include the lack of real-time captioning, the small sign language reading window, and misinterpretation of sign language. We present design considerations for improving the accessibility of the livestream platforms.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI .

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Section: Accessibility Of Information and Communication Technologies ...mentioning

confidence: 99%

Sparkling Silence: Practices and Challenges of Livestreaming Among Deaf or Hard of Hearing Streamers

Cao,

He,

Zhou

et al. 2023

Preprint

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show abstract

“…Te game encourages users to engage with a virtual environment, allowing them to learn sign language in a novel and enjoyable way. It is regarded as a language of Portuguese Sign Language [23] to use sign language signals to consolidate similar aims and purposes. ISL translation system and Indian Sign Language (ISL) translation device for learning sign language pictures or continuous video images (of the public) are captured using a microphone or USB camera and can be interpreted by an application.…”

Section: Literature Reviewmentioning

confidence: 99%

A Computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset for Differently Abled People Using Artificial Intelligence

Zahid

Syed

Rashid

et al. 2023

Mobile Information Systems

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Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from the deaf person, and then the model crops the frames into pictures. Then, the individual picture is recognized for the sign symbol such as if the deaf showed a symbol for one, then the model recognizes it and shows the letter which he/she wants to tell. Image processing techniques such as OpenCV are used for image recognition and classification while TensorFlow and linear regression are used for training the model to behave intelligently in the future. The results show that the proposed model increased accuracy from 80% to 97% and 100% accordingly. The accuracy of the previously available work was 80% when we implemented the algorithms, while with the proposed algorithm, when we used linear regression, we achieved the highest accuracy. Similarly, when we used the TensorFlow deep learning algorithm, we achieved 97% accuracy which was less than that of the linear regression model.

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“…The requirements for these tools must be further studied in order to improve usability and usefulness [38]. Assistive technologies for the hearing-impaired must be designed to fit for all and more commercialised as many are only designed for research purposes [39,27,40]. Many solutions that are present are flawed as most are one-sided, they either work for a certain group of hearing deficient individuals (deaf, hard of hearing, or mute) or they work only under specifically predefined situations.…”

Section: Overview Of Current Toolsmentioning

confidence: 99%

Deaf Chat: A Speech-to-Text Communication Aid for Hearing Deficiency

Shezi¹,

Ade-Ibijola²

2020

Adv. sci. technol. eng. syst. j.

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Hearing impairments have a negative impact in the lives of individuals living with them and those around such individuals. Different applications and technological tools have been developed to help reduce this negative impact. Most mobile applications that have been developed that use Speech-to-Text technology have been inconsistent such that they are not inclusive of all types of hearing impaired individuals, only work under specifically predefined environments and do not support conversations with multiple participants. This makes the present tools less effective and makes hearing impaired participants feel like they are not completely part of the conversation. This paper presents a model that aims to address this by introducing the use of Multiple-Speaker Classification technology in the design of mobile applications for hearing impaired people. Furthermore we present a prototype of a mobile application called Deaf Chat that uses the newly designed model. A survey was conducted in order to evaluate the potential that this application has to address the needs of hearing-impaired people. The results of the evaluation presented a good user acceptance and proved that a platform like Deaf Chat could be useful for the greater good of those who have hearing impairment.

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Assistive technologies for severe and profound hearing loss: Beyond hearing aids and implants

Cited by 13 publications

References 67 publications

Sparkling Silence: Practices and Challenges of Livestreaming Among Deaf or Hard of Hearing Streamers

Sparkling Silence: Practices and Challenges of Livestreaming Among Deaf or Hard of Hearing Streamers

A Computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset for Differently Abled People Using Artificial Intelligence

Deaf Chat: A Speech-to-Text Communication Aid for Hearing Deficiency

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