To make the step from homeService to PALs requires spo- ken dialogues between the owner and the device. Dialogue management techniques in commercial dialogue systems are usually hand-crafted, which makes them difficult to adapt. Dur- ing the last decade it has become fashionable to approach the dialogue management problem statistically, modelling the dialogue as a Partially Observable Markov Decision Process (POMDP) and optimising the dialogue policy with Reinforce- ment Learning (RL) . This framework provides robustness against speech understanding errors and automatic learning of dialogue policy. As the dialogue policy is learned with the data gathered from interaction with the user, it is optimised for its specific user, making it a personalised policy. RL permits on- line learning, so the system can also adapt its policy to changes in the user behaviour (e.g. when the user becomes more familiar with the system) and to the changes in the speech understanding system (e.g. when the ASR improves as more data is gathered). The user can also explicitly give a reward to the system after each interaction, ‘teaching’ the system.
We are building a tool set to provide summary sta- tistics for measures designed by clinicians to screen, diagnose or provide training to patients. This will be achieved by extending an existing shareware software platform with “plug-ins” that perform specific measures and report results to the user. At present, our goal is to use the existing shareware software tool Wavesurfer (Wavesurfer, 2005). The new modules will be set up to report data from a single audio file, or groups of audio files in a standard table format, for easy input to statistical or other analysis software. For example, the data may be imported into a program that cor- relates speech data with scalp electrode and medi- cation data.
In this paper we explore the role of subjective well-being within the process of making together a personalized assistive device. Through a process of social product adaptation, assistive artifacts become part of occupational therapy and co-evolve with clients. Personal digital fabrication tools enable small user groups to make and share their one-of-a-kind products with the world. This approach opens up new possibilities for disabled people and their caregivers to actively engage with their own skills and challenges. The paper describes a case study of an inclusive participatory design approach, which leads to qualitative occupational experiences within the field of community- based practice. The aim is to show how the process of collaborative designing, making and using artifacts fosters several elements of subject well-being in itself. The starting point of this open design process is a threefold interaction involving industrial designers, patients and occupational therapists within their local product ecology. Co-experience driven design is an intersubjective process that enables all individual stakeholders to work on a common phenomenon in respect of each subjective experience. Participatory prototyping is applied as a mobilization medium that (a) coordinates and (b) motivates design actions towards collaborative well-being equilibriums. This form of artifact-mediated participatory design embodies simultaneously (1) a communication language between all stakeholders that identifies meaningful goals, (2) an explorative process to attain and challenge these goals, (3) a selection of meaningful and engaging prototyping activities and (4) an appropriateness process with local skills and technology. By implementing this creative process, disabled people and their carers become conscious actors in providing collaborative maintenance of their own physical, mental and social well-being.
Accessibility. A service delivery system is accessible when no one is excluded from the services or in any other way discriminated against. It is essential that the system is driven by user needs and that funds are available to remove financial barriers. People should know that there is a service delivery system, that assistive products exist, and where to go to access the system. It should be easy to obtain appropriate AT solutions without unnecessary delay. Elements of accessibility are the scope of the system (who is eligible), its simplicity, the availability of information to the public, financial barriers and costs for the user, duration of the process and the complexity of procedures. Competence. Professionals involved must have the knowledge and skills needed to properly meet the user needs. Competence is about the availability of knowledge, skills and experience necessary to serve the client. Elements are the educational level of professionals, the possibilities for further education, the use of protocols and standards, the availability of information and the possibility to learn from feedback.
is much faster than typing on a touchscreen, while typing on a computer keyboard is seemingly easier and faster. However, even a few years ago speech recognition software was criticised due to its error-prone performance which inevitably lead to spending too much time correcting the mistakes. It therefore seemed reasonable to assume that professionals who use a keyboard as part of their daily routine, translators included, would not be inclined to integrate into their work technologies which actually slow them down. However, a lot has changed since then: Nuance has produced Dragon Speech Recognition software, one of the leading speech recognition technologies, and claims that it is now able to transcribe up to 160 words per minute, which is also about three times faster than typing, with an enviable 99% recognition accuracy (cf. Dragon NaturallySpeaking 2 ). This suggests speech technologies are now much
Assistivetechnology helps individuals with disabilities to perform functions that might otherwise be difficult or impossible. Assistivetechnology includes mobility devices such as walkers and wheelchairs, as well as hardware, software, and peripherals that assist people with disabilities in accessing computers or other information technologies (Access IT University of Washington, 2010). For instance, people who have limited hand function may use a keyboard with large keys or a special mouse to operate a computer. People who are visually impaired may use software like JAWS (Job Assist With Speech) that reads text on the screen in a computer-generated voice, or software that enlarges screen content. Those who are deaf may use a TTY (text telephone), and those with speech impairments may use a device that speaks out loud as they enter text via a keyboard (Access IT University of Washington, 2010).
Tap on/ provide speech input to walk through obstacle then switching the system on the device gets ready to detect the obstacles. Here the IR sensors on the hardware device placed on its Left, right and front side emits the radiation continuously. When any obstacle appears near the device the rays emitted by the IR will be reflected back on the IR receiver and the signal is then transmitted to the ADC. ADC converts the analog signal into digital signals that are required for the further computation. Atmega32 microcontroller accepts these digital signals and performs computation which will compare these values with the threshold value. If the value is less than the threshold value the obstacle is detected and an alert message is given by the smart phone in the form of speech output. Simultaneously the status of the obstacle detected is updated on the server. Meanwhile if any mishap happens and there is a sudden fall the built in accelerometer in smart phone collects the x, y, z- coordinate values. Euclidian distance formula is applied to
systems as it has to search the whole dictionary and pronunciation may vary. P. Sanja et.al.  proposed speech based SMS system on Android which uses HMM Method to send SMS. The advantage of the system is it can be used for recognizing various types of speech from the users according to their voice modulation. The disadvantage is the time taken for recognizing the speech is more for simpler systems. V. Sharon  proposed different ways for improving differently abled people using IoT technology. Proposed ways how an assistivetechnology can change the lives of differently abled people and how they can reduce the dependency on other people. B.K. Bhoomika et.al.  proposed an IoT based smart secured health care monitoring system using compression techniques in transferring the messages as the data related to the patients are very important and loss of data in emergency may provide danger to the patient. S.M. Raizul et.al. proposed Comprehensive Survey on Health Care with different methods for accessing and transferring the data of the patient are mentioned and different parameters are used to monitor the health of different kinds of patients. T. Sapna et al.  proposed the use of IoT in health care sector and its technological aspects. This system developes network among all entities communicating to the cloud. A. Alexandru et al.  developed a monitoring system prototype which focused on remote patient monitoring in wards, following an ICU discharge using sensor arrays for EKG, SpO2, temperature and movement. S. M. Riazul et al.  proposed an collaborative security model using different technologies addressing various IoT and eHealth along with IoT security and privacy features. M. Hasmah, et.al.  proposed body Temperature measurement for remote monitoring, the patient’s temperature can be sensed remotely and medication can be prescribed by the doctor immediately. Manohar, S.et.al  Proposed E-Mail based Interaction for Home Automation System. The disadvantage of the system is the user may not check his emails very frequently and some mails may not reach the recipient which in turn effects the performance of the system.
In this paper, various SpeechAssistive Techniques are discussed which are generally used for mild to normal dysarthric speakers. Speech recognition technique with HMM, GMM or MFCC generally uses supervised learning in which the trained data helps in identifying the accurate words which are grafted or concatenated with the original phonemes of the dysarthric speaker. ALADIN approach is used to mine recurrent acoustic patterns from weakly supervised dysarthric speech data, to achieve two goals: 1) Achieving usable recognition accuracies with less training data, in order to minimize the initial effort of the target user, and 2) Achieving usable recognition accuracies with less detailed annotation - training a vocal interface using an unordered list of semantic concepts that are contained in the sentences, rather than a word-by-word transcript. Generally for severe dysarthric speakers these techniques fail to give accuracy more than 90%. The future enhancements can be the speechassistivetechnology which may use an unsupervised approach and give a better accuracy with minimum time.
Assistivetechnology enables people with disabilities to achieve tasks that they may otherwise be unable to do. Environmental control systems are one of the most well established forms of electronic assistivetechnology in the United Kingdom. They provide people with severe disabilities the ability to control their home environment, for example operating the television or answering the phone. In the United Kingdom, environmental control systems are generally provided through NHS electronic assistivetechnology services or community occupational therapy teams. Generally assessments for provision of systems are carried out by clinical scientists or specialist occupational therapists and provided to people with severe disabilities affecting the use of their upper limbs. Due to the nature of the target user’s disabilities such systems are generally controlled (accessed) using a single switch – this can potentially provide access to the full range of household equipment using even very small functional movements.
During this process it is also important to acknowledge the individual's environment. For example, if the individual is in a strange environment they may have less co ordination, due to being uncomfortable in their present surroundings. Other factors that must be taken into consideration include: the individual’s position e.g., sitting, lying or standing, as these elements may be enabling or prohibiting movement, or visual clues. Often the process “Previous equipment usage” can be examined at the same time as the process “Define abilities/disabilities” while the professionals are in the company of the individual concerned. “Previous equipment usage” can in general assist in defining the individual’s potential. This process may give the assessor ideas of possible alternative avenues to investigate with regard to the individual’s ability to interact. This in turn will reduce the required time for completion of the prescription. In addition, although the equipment previously used may have been the latest technology when it was provided, that technology may now be out of date and the technician may suggest more up to date technology that he/she would consider being a suitable alternative. For example, in the past an individual may have been using a scanning system with a switch (Vanderheiden, (1988)) to word process. However, the individual could have vocal ability to use speech recognition that is now available. It may be that both speech recognition and switch control could give the user a higher potential. In this case speech recognition does not replace, but supplements, the user interface.
The recognition service itself is built on top of the Kaldi speech recognition toolkit , a state of the art solution licensed as free-software, with a modular design and an active developer community. Interaction with Kaldi is done using GStreamer, using a design that was based on Tanel AlumaeÕs kaldi-gstreamer-server [[Alumae2016]], but modified to suit the specific needs of the CloudCAST project. In particular, this design makes it possible to separate the handling of incoming connections, a task of the server, from the processing of the speech data, which is delegated to separate worker processes ensuring the scalability of the project.
ken dialogue system in a partially observable Markov decision process (POMDP) framework, in which the the dialogue sys- tem seeks to infer the user’s intent and handles speech recog- nition uncertainty by asking confirmation questions. We learn models of: 1) how speech recognition hypotheses map to user intents and 2) meaningful confidence scores from ASR features so that our dialogue manager can make better response deci- sions. Our work draws on modeling techniques from work in spoken dialogue system POMDPs (e.g., [4, 5, 6]) and is inspired by other POMDP-based assistive technologies for handwashing (e.g., ) and intelligent wheelchair navigation (e.g., [8, 9, 10]), all of which model the user’s intent as a hidden state to be in- ferred from observations.
Dr. Layton (Australia): AT as a key occupational therapy strategy is taught in occupational therapy school curricula. Professional development events on AP are run by Occupational Therapy Australia divisions and by technology suppliers and are highly sought after. Occupational therapists are prominent in interdisciplinary conversations about AT—for example, leading transdisciplinary practice approaches in public health; working toward credentialed AT training opportunities through the peak body, Australian Rehabilitation and AssistiveTechnology Association (ARATA); and researching and coproducing evidence on AT use at relevant conferences. Good practice steps of initiation, evaluation, trialing, adapting and training, providing, maintaining, and reviewing are built into occupational therapy management plans. Occupational therapy and AT go hand-in-hand in Australia. Scope of practice discussions continue around boundary products, such as mobility devices and physiotherapy, pressure care and nursing, and switch access to communication devices and speech pathology. But a highly collegial sector (evidenced by the presence of our Allied Health Professions Association, AHPA)
Microsoft Internet Explorer) and an Application Programming Interface (eg. Microsoft Active Accessibility) screen readers use the source code of a web page to construct an alternative, accessible representation of the page and the functional components it contains. When a page is coded correctly, most screen readers are able to present (either through speech or Braille) the text on a web page, alternative descriptions for images and multimedia content, as well as identifying headings, lists items, links, frames, tables and form elements. The most widely used screen readers in Australia are JAWS (available from Freedom Scientific) and Window-Eyes (GW-Micro). HAL and Supernova (Dolphin Computer Access) and LookOut (Choice Technology) are also used in Australia, but more widely used in other countries, particularly in Europe. Recent versions of the Windows and Apple operating systems have built-in screen readers, but the features are limited so they are not widely used by people who depend on a screen reader to access the Web.
investments in research about this issue since the 1950s in countries in North America and Europe, in Brazil that investment is low and the use of assistive technologies is still limited. According to Mello, the main factors that contributed to low use were: (a) the absence of financial resources for device acquisition, (b) insufficient funding for assistivetechnology services by public health organizations and private health businesses, (c) rehabilitation professionals’ lack of technical knowledge
Barnett et al. (1997) and Crestani (2000) indepen- dently performed comparative experiments related to speech-driven retrieval, where the DRAGON speech recognition system was used as an input in- terface for the INQUERY text retrieval system. They used as test queries 35 topics in the TREC col- lection, dictated by a single male speaker. How- ever, these cases focused on improving text retrieval methods and did not address problems in improv- ing speech recognition. As a result, errors in recog- nizing spoken queries (error rate was approximately 30%) considerably decreased the retrieval accuracy. Although we showed that the use of target docu- ment collections in producing language models for speech recognition significantly improved the per- formance of speech-driven retrieval (Fujii et al., 2002; Itou et al., 2001), a number of issues still re- main open questions.
This study examined low-level aspects of speech recognition among older adults with Alzheimer’s disease interacting with a robot in a simulated home environment. The best word-level accura- cies of 40.9% (σ = 5.6) and 39.2% (σ = 6.3) achievable with noise reduction and in a quiet in- terview setting are comparable with the state-of- the-art in unrestricted large-vocabulary text entry. These results form the basis for ongoing work in ASR and interaction design for this domain. The trigram language model used in this work encap- sulates the statistics of a large amount of speech from the general population – it is a speaker- independent model derived from a combination of English news agencies that is not necessarily representative of the type of language used in the home, or by our target population. The acoustic models were also derived from newswire data read by younger adults in quiet environments. We are currently training and adapting language models tuned specifically to older adults with Alzheimer’s disease using data from the Carolina Conversa- tions database (Pope and Davis, 2011) and the De- mentiaBank database (Boller and Becker, 1983).
Welcome to the world of assistivetechnology and special education in the Austin Public Schools! This year, a district- wide committee was formed to deal with assistivetechnology. We have put together this manual to help you address this important issue. This manual that we have put together will not replace the Minnesota state manual, but should be used to help clarify and support those efforts. The Minnesota state manual can be found at