Mobile Sound Recognition for the Deaf and Hard of Hearing

Human perception of surrounding events is strongly dependent on audio cues. Thus, acoustic insulation can seriously impact situational awareness. We present an exploratory study in the domain of assistive computing, eliciting requirements and presenting solutions to problems found in the development of an environmental sound recognition system, which aims to assist deaf and hard of hearing people in the perception of sounds. To take advantage of smartphones computational ubiquity, we propose a system that executes all processing on the device itself, from audio features extraction to recognition and visual presentation of results. Our application also presents the confidence level of the classification to the user. A test of the system conducted with deaf users provided important and inspiring feedback from participants.Comment: 25 pages, 8 figure

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

Assistive technologies offer capabilities that were previously inaccessible to individuals with severe and profound hearing loss who have no or limited access to hearing aids and implants. This literature review aims to explore existing assistive technologies and identify what still needs to be done. It is found that there is a lack of focus on the overall objectives of assistive technologies. In addition, several other issues are identified i.e. only a very small number of assistive technologies developed within a research context have led to commercial devices, there is a predisposition to use the latest expensive technologies and a tendency to avoid designing products universally. Finally, the further development of plug-ins that translate the text content of a website to various sign languages is needed to make information on the internet more accessible

A new system to detect coronavirus social distance violation

In this paper, a novel solution to avoid new infections is presented. Instead of tracing users’ locations, the presence of individuals is detected by analysing the voices, and people’s faces are detected by the camera. To do this, two different Android applications were implemented. The first one uses the camera to detect people’s faces whenever the user answers or performs a phone call. Firebase Platform will be used to detect faces captured by the camera and determine its size and estimate their distance to the phone terminal. The second application uses voice biometrics to differentiate the users’ voice from unknown speakers and creates a neural network model based on 5 samples of the user’s voice. This feature will only be activated whenever the user is surfing the Internet or using other applications to prevent undesired contacts. Currently, the patient’s tracking is performed by geolocation or by using Bluetooth connection. Although face detection and voice recognition are existing methods, this paper aims to use them and integrate both in a single device. Our application cannot violate privacy since it does not save the data used to carry out the detection and does not associate this data to people

Crowdsourcing the Perception of Machine Teaching

Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N = 100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.Comment: 10 pages, 8 figures, 5 tables, CHI2020 conferenc

Keskusteluavustimen kehittäminen kuulovammaisia varten automaattista puheentunnistusta käyttäen

Understanding and participating in conversations has been reported as one of the biggest challenges hearing impaired people face in their daily lives. These communication problems have been shown to have wide-ranging negative consequences, affecting their quality of life and the opportunities available to them in education and employment. A conversational assistance application was investigated to alleviate these problems. The application uses automatic speech recognition technology to provide real-time speech-to-text transcriptions to the user, with the goal of helping deaf and hard of hearing persons in conversational situations. To validate the method and investigate its usefulness, a prototype application was developed for testing purposes using open-source software. A user test was designed and performed with test participants representing the target user group. The results indicate that the Conversation Assistant method is valid, meaning it can help the hearing impaired to follow and participate in conversational situations. Speech recognition accuracy, especially in noisy environments, was identified as the primary target for further development for increased usefulness of the application. Conversely, recognition speed was deemed to be sufficient and already surpass the transcription speed of human transcribers.Keskustelupuheen ymmärtäminen ja keskusteluihin osallistuminen on raportoitu yhdeksi suurimmista haasteista, joita kuulovammaiset kohtaavat jokapäiväisessä elämässään. Näillä viestintäongelmilla on osoitettu olevan laaja-alaisia negatiivisia vaikutuksia, jotka heijastuvat elämänlaatuun ja heikentävät kuulovammaisten yhdenvertaisia osallistumismahdollisuuksia opiskeluun ja työelämään. Työssä kehitettiin ja arvioitiin apusovellusta keskustelupuheen ymmärtämisen ja keskusteluihin osallistumisen helpottamiseksi. Sovellus käyttää automaattista puheentunnistusta reaaliaikaiseen puheen tekstittämiseen kuuroja ja huonokuuloisia varten. Menetelmän toimivuuden vahvistamiseksi ja sen hyödyllisyyden tutkimiseksi siitä kehitettiin prototyyppisovellus käyttäjätestausta varten avointa lähdekoodia hyödyntäen. Testaamista varten suunniteltiin ja toteutettiin käyttäjäkoe sovelluksen kohderyhmää edustavilla koekäyttäjillä. Saadut tulokset viittaavat siihen, että työssä esitetty Keskusteluavustin on toimiva ja hyödyllinen apuväline huonokuuloisille ja kuuroille. Puheentunnistustarkkuus erityisesti meluisissa olosuhteissa osoittautui ensisijaiseksi kehityskohteeksi apusovelluksen hyödyllisyyden lisäämiseksi. Puheentunnistuksen nopeus arvioitiin puolestaan jo riittävän nopeaksi, ylittäen selkeästi kirjoitustulkkien kirjoitusnopeuden

Human Computer Interaction and Emerging Technologies

The INTERACT Conferences are an important platform for researchers and practitioners in the field of human-computer interaction (HCI) to showcase their work. They are organised biennially by the International Federation for Information Processing (IFIP) Technical Committee on Human–Computer Interaction (IFIP TC13), an international committee of 30 member national societies and nine Working Groups. INTERACT is truly international in its spirit and has attracted researchers from several countries and cultures. With an emphasis on inclusiveness, it works to lower the barriers that prevent people in developing countries from participating in conferences. As a multidisciplinary field, HCI requires interaction and discussion among diverse people with different interests and backgrounds. The 17th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2019) took place during 2-6 September 2019 in Paphos, Cyprus. The conference was held at the Coral Beach Hotel Resort, and was co-sponsored by the Cyprus University of Technology and Tallinn University, in cooperation with ACM and ACM SIGCHI. This volume contains the Adjunct Proceedings to the 17th INTERACT Conference, comprising a series of selected papers from workshops, the Student Design Consortium and the Doctoral Consortium. The volume follows the INTERACT conference tradition of submitting adjunct papers after the main publication deadline, to be published by a University Press with a connection to the conference itself. In this case, both the Adjunct Proceedings Chair of the conference, Dr Usashi Chatterjee, and the lead Editor of this volume, Dr Fernando Loizides, work at Cardiff University which is the home of Cardiff University Press