Multilingual Speech Recognition for the Elderly: The AALFred Personal Life Assistant

Hämäläinen, Annika; Teixeira, António; Almeida, Nuno; Meinedo, Hugo; Fegyó, Tibor; Dias, Miguel Sales

doi:10.1016/j.procs.2015.09.272

Cited by 21 publications

(6 citation statements)

References 9 publications

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“…The capabilities and versatility of the architecture and framework were demonstrated multiple times, notable examples being: (1) their use to create multimodal complex systems and modalities, e.g., a multimodal interactive speech-gaze personal assistant [67]; (2) their adoption as the architecture and framework for the development of a multimodal assistant in the scope of European project AAL PaeLIFE [68,69], involving multiple development teams spread over Europe; (3) their use, as the basis for a graduate course, by the authors, in Multimodal interaction, for several years (in their multiple iterations). As an example of the works developed by students, the reader is forwarded to [70], describing a multimodal presentation system made possible by the AM4I framework and developed in a few weeks of work (illustrative video available at ).…”

Section: Discussionmentioning

confidence: 99%

The AM4I Architecture and Framework for Multimodal Interaction and Its Application to Smart Environments

Almeida

Teixeira

Silva

et al. 2019

Sensors

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Technologies, such as smart sensors, actuators, and other kinds of devices, are often installed in our environments (e.g., our Homes) and available to integrate our daily lives. Despite their installation being motivated by the pursuit of automation and increased efficiency, making these environments usable, acceptable and enjoyable in a sustainable, energy efficient way is not only a matter of automation. Tackling these goals is a complex task demanding the combination of different perspectives including building and urban Architecture, Ubiquitous Computing and Human-Computer Interaction (HCI) to provide occupants with the means to shape these environments to their needs. Interaction is of paramount relevance in the creation of adequate relations of users with their environments, but it cannot be seen independently from the ubiquitous sensing and computing or the environment’s architecture. In this regard, there are several challenges to HCI, particularly in how to integrate this multidisciplinary effort. Although there are several solutions to address some of these challenges, the complexity and dynamic nature of the smart environments and the diversity of technologies involved still present many challenges, particularly for its development. In general, the development is complex, and it is hard to create a dynamic environment providing versatile and adaptive forms of interaction. To participate in the multidisciplinary effort, the development of interaction must be supported by tools capable of facilitating co-design by multidisciplinary teams. In this article, we address the development of interaction for complex smart environments and propose the AM4I architecture and framework, a novel modular approach to design and develop adaptive multiplatform multilingual multi-device multimodal interactive systems. The potential of the framework is demonstrated by proof-of-concept applications in two different smart environment contexts, non-residential buildings and smart homes.

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Section: Discussionmentioning

confidence: 99%

The AM4I Architecture and Framework for Multimodal Interaction and Its Application to Smart Environments

Almeida

Teixeira

Silva

et al. 2019

Sensors

Self Cite

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“…As a result, a feedback loop is integrated in the network. Recurrent neural networks are mainly used for speech recognition (Hamalainen et al, 2015) and other applications that include time-series, such as stock price predictions (Kumar et al 2021) . Regular feed forward networks are powerful on structured datasets with relatively small number of features.…”

Section: B2 Neural Network (Fnn Rnn Cnn)mentioning

confidence: 99%

Machine learning and genetic algorithms for conformal geometries in design support systems

Hassel¹,

Hofmeyer²,

Ezendam³

et al. 2023

ECPPM 2022 - eWork and eBusiness in Architecture, Engineering and Construction 2022

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“…The application can be conducted employing speech input in four different European languages, including French, Hungarian, Polish and Portuguese. This research concisely introduces the personal life assistant and later emphasizes the speech-involved accomplishments of the project such as the collection, transcription and annotation of huge corpora of elderly speech, the invention of automatic speech recognizers revised for elderly speakers, a speech modality factor that can conveniently be used again in other applications, and an automatic grammar translation service which permits a rapid increase in the application of automatic speech recognition in new languages [10]. The study of the development of the Automatic Speech Recognition Techniques is applied to the mobile application development for seniors in order to support the elderly way of living in different aspects of life.…”

Section: Automatic Speech Recognition Techniquesmentioning

confidence: 99%

Mobile Application Development of Managing Elderly Household Accounts Using Speech Recognition

Nuanmeesri

2020

Int. J. Interact. Mob. Technol.

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Using spoken language is the easiest and most natural way of communication for human beings. The communication between humans and machines using sounds or language is a huge move forward. This article demonstrates the development of a mobile application for managing household accounts with the integration of a speaking voice and touch-screen function on smartphones; this could also be used as a tool to manage household accounts for elderly Thai people. The content was evaluated by five experts to identify each feature of the developed mobile application. Afterwards, the developed mobile application was distributed to thirty-three elderly people. The findings showed that the elderly people were able to learn and use the mobile application effectively. The learning post-test results scored higher than the pre-test results. The effectiveness evaluation results in terms of the accessibility barriers that can be faced by elderly people while using mobile applications showed that the average mean was 4.13 and the standard deviation was 0.36 and the acceptance of the mobile application based on accessibility barriers that could be detected by elderly people while being used has high consensus. This indicates that the developed mobile application is effective for elderly users at a high level.

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Multilingual Speech Recognition for the Elderly: The AALFred Personal Life Assistant

Cited by 21 publications

References 9 publications

The AM4I Architecture and Framework for Multimodal Interaction and Its Application to Smart Environments

The AM4I Architecture and Framework for Multimodal Interaction and Its Application to Smart Environments

Machine learning and genetic algorithms for conformal geometries in design support systems

Mobile Application Development of Managing Elderly Household Accounts Using Speech Recognition

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