The Impact of Assistive Technology on Communication Quality Between Deaf and Hearing Individuals

Gugenheimer, Jan; Plaumann, Katrin; Schaub, Florian; Vito, Patrizia Di Campli San; Duck, Saskia; Rabus, Melanie; Rukzio, Enrico

doi:10.1145/2998181.2998203

Cited by 37 publications

(35 citation statements)

References 29 publications

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“…It was found that while ATs can empower individuals with HL and provide a high level of autonomy when communicating with hearing individuals, they can also lead to breaks in conversations that generate discomfort for the user by emphasizing their disability and dependency on technology for communication [34]. Such findings demonstrate the variety of factors that influence the use of AT and provides a background on why people might be motivated to engage in designing their own HL solutions.…”

Section: User Attitudes Towards Assistive Technologiesmentioning

confidence: 93%

Social, Cultural and Systematic Frustrations Motivating the Formation of a DIY Hearing Loss Hacking Community

O’Kane

Aliomar

Zheng

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Research on attitudes to assistive technology (AT) has shown both the positive and negative impact of these technologies on quality of life. Building on this research, we examine the sociocultural and technological frustrations with hearing loss (HL) technologies that motivate personal approaches to solving these issues. Drawing on meet-up observations and contextual interview data, we detail participants' experiences of and attitudes towards hearing AT that influences hacking hearing loss. Hearing AT is misunderstood as a solution to the impairment, influencing one-to-one interactions, cultural norms, and systematic frustrations. Participants' exasperation with the slow development of top-down solutions has led some members to design and develop their own personalised solutions. Beyond capturing a segment of the growing DIY health and wellbeing phenomenon, our findings extend beyond implications for design to present recommendations for the hearing loss industry, policy makers, and importantly, for researchers engaging with grassroots DIY health movements. CCS CONCEPTS • Human centered computing → Human computer interaction (HCI); Field Studies; Empirical studies in HCI; • Human centered computing → Collaborative and social computing; • Human centered computing → Accessibility; Empirical studies in accessibility.

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Section: User Attitudes Towards Assistive Technologiesmentioning

confidence: 93%

Social, Cultural and Systematic Frustrations Motivating the Formation of a DIY Hearing Loss Hacking Community

O’Kane

Aliomar

Zheng

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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“…Short‐term energy is the principal and most common feature, which has been utilized in signal and speech processing applications. The energy of a signal is generally computed over a short term basis, through the windowing of the signal at a certain point of time, squaring the samples, and considering the average 27,28 . The square root of this result is known as the root‐mean square (RMS) value and is also applied.…”

Section: Proposed Methodologymentioning

confidence: 99%

Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK‐SVM) classification technique for speech recognition in hearing‐impaired listeners

Pavithra

Janakiraman

2019

Concurrency and Computation

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Summary Automatic speech recognition of Tamil Language with Hearing‐Impaired becomes difficult task in recent decades. In order to deal with the challenges with speech perception in hostile listening situations, Noise Reduction (NR) algorithms have been developed with the aim of improving the speech intelligibility (SI), speech quality, and ease of listening. Even though the noises are removed, extraction of correct features from the speech becomes difficult task. The major aim of this work is to introduce a Classification Technique for Speech Recognition in Hearing‐Impaired Listeners. The binary mask along with its binary weights and the Wiener filter with constant weights form the representatives of a hard and a soft‐decision scheme for time‐frequency masking. In the proposed Log Frequency Power Coefficients (LFPC), Pitch, Mel‐Frequency Cepstral Coefficients (MFCCs), Energy, formants, and intensity as input feature vectors are extracted from preprocessed signals. Then, for automatic speech recognition process, Enhanced Polynomial Kernel (EPK)–based Support Vector Machine (SVM) (EPK‐SVM) classifier is proposed for Hearing Impaired in Tamil language is implemented in MATLAB software. The results obtained showed that SVM is found to be potential in hearing‐impaired application and is validated via the use of the recognition accuracy and error rate.

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“…Communication difficulties are ubiquitous in face-to-face interactions especially for persons with deafness when they interact with Non-PWDs. Gugenheimer et al (2017) 2017) is made aware that their approach needs to be changed. Instead of implying that persons with deafness need to "hear," they must teach Non-PWDs their need or capability to communicate in sign language.…”

Section: Deaf Non-verbal Needs and Communication Patternmentioning

confidence: 99%

DEAFying Silence: Analyzing Inclusive Non-Verbal Communication Strategies Between Students with Deafness and Their Non-PWD Peers

Taguibulos¹

2020

Preprint

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People would often attribute persons with deafness as ‘mute’ individuals due to their frequent use of non-verbal communication strategies. In the Philippines’ transition to an inclusive education system, the question is how Non-PWDs can accommodate the communication style and needs of students with deafness. The researcher answers the question: How do students with deafness and Non-PWD peers promote inclusion through non-verbal communication strategies? A qualitative research design was utilized as the framework, while the research procedure consisted of a digital semi-structured interview for the participant with deafness and a focus group discussion for the Non-PWD participants. A thematic data analysis was carried out to examine the findings and extract themes.The emerging themes revealed that students with deafness are provided with poor assistive services, and that there are issues involving the lack of social inclusivity in mainstream schools. Non-PWDs have shown initiative to shatter communication barriers by obliterating the stigma of communicating with students with deafness. Conversely, the status quo is that persons with deafness should apply spoken language instead of encouraging Non-PWDs to learn sign language. It has been concluded that persons with deafness are visual communicators. They use space to distinguish their relationship with another communicator, and they also apply kinesic behavior like sign language to deliver their messages, facial expressions to convey their emotions, and body language to further demonstrate their messages. Lastly, Non-PWDs should be enhancing their usage of time, silence, and developing their application of space to practice haptics.

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The Impact of Assistive Technology on Communication Quality Between Deaf and Hearing Individuals

Cited by 37 publications

References 29 publications

Social, Cultural and Systematic Frustrations Motivating the Formation of a DIY Hearing Loss Hacking Community

Social, Cultural and Systematic Frustrations Motivating the Formation of a DIY Hearing Loss Hacking Community

Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK‐SVM) classification technique for speech recognition in hearing‐impaired listeners

DEAFying Silence: Analyzing Inclusive Non-Verbal Communication Strategies Between Students with Deafness and Their Non-PWD Peers

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