44,234 research outputs found
Meeting of the MINDS: an information retrieval research agenda
Since its inception in the late 1950s, the field of Information Retrieval (IR) has developed tools that help people find, organize, and analyze information. The key early influences on the field are well-known. Among them are H. P. Luhn's pioneering work, the development of the vector space retrieval model by Salton and his students, Cleverdon's development of the Cranfield experimental methodology, SpÀrck Jones' development of idf, and a series of probabilistic retrieval models by Robertson and Croft. Until the development of the WorldWideWeb (Web), IR was of greatest interest to professional information analysts such as librarians, intelligence analysts, the legal community, and the pharmaceutical industry
Speech-Gesture Mapping and Engagement Evaluation in Human Robot Interaction
A robot needs contextual awareness, effective speech production and
complementing non-verbal gestures for successful communication in society. In
this paper, we present our end-to-end system that tries to enhance the
effectiveness of non-verbal gestures. For achieving this, we identified
prominently used gestures in performances by TED speakers and mapped them to
their corresponding speech context and modulated speech based upon the
attention of the listener. The proposed method utilized Convolutional Pose
Machine [4] to detect the human gesture. Dominant gestures of TED speakers were
used for learning the gesture-to-speech mapping. The speeches by them were used
for training the model. We also evaluated the engagement of the robot with
people by conducting a social survey. The effectiveness of the performance was
monitored by the robot and it self-improvised its speech pattern on the basis
of the attention level of the audience, which was calculated using visual
feedback from the camera. The effectiveness of interaction as well as the
decisions made during improvisation was further evaluated based on the
head-pose detection and interaction survey.Comment: 8 pages, 9 figures, Under review in IRC 201
Recommended from our members
Increasing the intensity and comprehensiveness of aphasia services: identification of key factors influencing implementation across six countries
Background: Aphasia services are currently faced by increasing evidence for therapy of greater intensity and comprehensiveness. Intensive Comprehensive Aphasia Programs (ICAPs) combine these elements in an evidence-based, time-limited group program. The incorporation of new service delivery models in routine clinical practice is, however, likely to pose challenges for both the service provider and administering clinicians. This program of research aims to identify these challenges from the perspective of aphasia clinicians from six countries and will seek to trial potential solutions. Continual advancements in global communication technologies suggest that solutions will be easily shared and accessed across multiple countries.
Aims: To identify the perceived and experienced barriers and facilitators to the implementation of 1) intensive aphasia services, 2) comprehensive aphasia services, and 3) ICAPs, from aphasia clinicians across six countries.
Methods and procedures: A qualitative enquiry approach included data from six focus groups (n = 34 participants) in Australia, New Zealand, Canada, United States of America (USA), United Kingdom (UK), and Ireland. A thematic analysis of focus group data was informed by the Theoretical Domains Framework (TDF).
Outcomes and results: Five prominent theoretical domains from the TDF influenced the implementation of all three aphasia service types across participating countries: environmental context and resources, beliefs about consequences, social/professional role and identity, skills, and knowledge. Four overarching themes assisted the identification and explanation of the key barriers and facilitators: 1. Collaboration, joint initiatives and partnerships, 2. Advocacy, the promotion of aphasia services and evidence-based practice, 3. Innovation, the ability to problem solve challenges, and 4. Culture, the influence of underlying values.
Conclusions: The results of this study will inform the development of a theoretically informed intervention to improve health servicesâ adherence to aphasia best practice recommendations
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
Most speech and language technologies are trained with massive amounts of
speech and text information. However, most of the world languages do not have
such resources or stable orthography. Systems constructed under these almost
zero resource conditions are not only promising for speech technology but also
for computational language documentation. The goal of computational language
documentation is to help field linguists to (semi-)automatically analyze and
annotate audio recordings of endangered and unwritten languages. Example tasks
are automatic phoneme discovery or lexicon discovery from the speech signal.
This paper presents a speech corpus collected during a realistic language
documentation process. It is made up of 5k speech utterances in Mboshi (Bantu
C25) aligned to French text translations. Speech transcriptions are also made
available: they correspond to a non-standard graphemic form close to the
language phonology. We present how the data was collected, cleaned and
processed and we illustrate its use through a zero-resource task: spoken term
discovery. The dataset is made available to the community for reproducible
computational language documentation experiments and their evaluation.Comment: accepted to LREC 201
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
Surveying Persons with Disabilities: A Source Guide (Version 1)
As a collaborator with the Cornell Rehabilitation Research and Training Center on Disability Demographics and Statistics, Mathematica Policy Research, Inc. has been working on a project that identifies the strengths and limitations in existing disability data collection in both content and data collection methodology. The intended outcomes of this project include expanding and synthesizing knowledge of best practices and the extent existing data use those practices, informing the development of data enhancement options, and contributing to a more informed use of existing data. In an effort to provide the public with an up-to-date and easily accessible source of research on the methodological issues associated with surveying persons with disabilities, MPR has prepared a Source Guide of material related to this topic. The Source Guide contains 150 abstracts, summaries, and references, followed by a Subject Index, which cross references the sources from the Reference List under various subjects. The Source Guide is viewed as a âliving document,â and will be periodically updated
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources
Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations
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