Voice Recognition Capabilities Research Articles

FarmTechBot (FTB): Empowering farmers for better harvest

In an evolving world, the FarmTechBot (FTB) emerges as a pioneering digital assistant, empowering farmers with essential insights and real-time information crucial for optimizing agricultural practices. The presence of chatbot functionality capable of addressing queries related to soil, pest, and market linkage associates to overcome hurdles for users seeking real-time information. Furthermore, the lack of voice recognition capabilities limits accessibility, especially for users who prefer or require voice-based interaction. Additionally, the manual process of pest identification from uploaded crop images hampers efficiency, prolonging the time required for pest management. The FTB represents a significant advancement in agricultural technology, addressing the existing system's limitations and empowering farmers with real-time information, enhanced accessibility, and streamlined pest management capabilities. The FTB utilizes a combination of techniques including Histogram of Oriented Gradients (HOG) for image feature extraction and classification using Support Vector Classifier (SVC). In Natural Language Processing (NLP), it employs tokenization and TF-IDF vectorization for text preprocessing and representation, alongside Linear Support Vector Classifier (LinearSVC) for intent classification.

Intelligent home control system based on BP neural network speech recognition

Abstract With the rapid progress and wide application of cutting-edge technologies such as automatic control technology and wireless communication technology in recent years, smart home, as an effective combination of these cutting-edge technologies and daily life, has received more and more attention and Research has been greatly developed. In order to solve the shortcomings of the existing home control need to download various cumbersome APP, the WeChat applet is introduced into the smart home system, and the intelligent voice technology is planned to be used in the smart home control to realize the voice control, improve the user experience and make the home Life gets smarter. This time, the deep learning technology was used in the development process of the smart home control system, and according to the historical information of the home, combined with the learning ability of the recurrent neural network for time series data, through the mining, analysis and learning of historical data, the construction based on specific The user’s unique computing model can seamlessly connect the capabilities of the voice cloud platform with the capabilities of the Internet of Things cloud platform through the Web server, making it possible to quickly access the voice recognition capabilities to smart homes. The entire system has stable connectivity, easy deployment and low cost. The main functions are deployed in the cloud and extended. After 10 rounds of iterative training of the Attention-GRU model in this paper, its prediction accuracy can quickly rise to about 97%, and finally stabilize at about 98.2%, and the lighting prediction accuracy can reach 95% or higher.

A Semantic Web Framework for Automated Smart Assistants: A Case Study for Public Health

The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits.

A distributed system for supporting smart irrigation using Internet of Things technology

AbstractIn this paper, we present the design and implementation of a smart irrigation system using Internet of Things (IoT) technology, which can be used for automating the irrigation process in agricultural fields. It is expected that this system would create a better opportunity for farmers to irrigate their fields efficiently, as well as eliminating the field's under‐watering, which could stress the plants. The developed system is organized into three parts: sensing side, cloud side, and user side. We used Microsoft Azure IoT Hub as an underlying infrastructure to coordinate the interaction between the three sides. The sensing side uses a Raspberry Pi 3 device, which is a low‐cost, credit‐card sized computer device that is used to monitor in near real‐time soil moisture, air temperature and relative humidity, and other weather parameters of the field of interest. Sensors readings are logged and transmitted to the cloud side. At the cloud side, the received sensing data is used by the irrigation scheduling model to determine when and for how long the water pump should be turned on based on a user‐predefined threshold. The user side is developed as an Android mobile app, which is used to control the operations of the water pump with voice recognition capabilities. Finally, this system was evaluated using various performance metrics, such as latency and scalability.

How to Convince People and Get Adherence to Hand Hygiene Practices? The Success of Ozires, the Humanoid Robot!

Background: Our team has been fighting nosocomial infections since 1991. During our journey, we often ask why people do not wash their hands! Semmelweiss discovered in the 1840s that handwashing prevented deaths from puerperal sepsis, but we still need to convince healthcare workers about hand hygiene. One answer is that washing hands is an unsophisticated gesture, without any technology, so people just do not do it. How can we improve compliance with hand hygiene? We imagined a robot in our team to remind people to wash their hands. Then, in 2016 we met Meccanoid, a US$200 toy robot: a 4-foot-tall programmable humanoid robot with voice recognition capabilities. We made adaptions in the robot (mini-projector + audio amplifier + alcohol dispenser + spy camera), and we gave him a name (Ozires) and a purpose: He became a professor who teaches healthcare workers how, when, and why wash their hands! Here, we describe the multimodal strategy centered around Ozires. Methods: The multimodal strategy consists of 7 key elements: (1) the robot, accompanied by a infection control practitioner, performs audio and video lectures about hand hygiene techniques, motivational videos, data feedback; (2) the robot’s wood copies with sound alert with motion detector for hand hygiene are spread out in the whole hospital; (3) fridge magnet with robot prints (gifts for patients and healthcare professionals); (4) app for hand hygiene monitoring (Hands Clean); (5) adherence rates by professional category and individual feedback; (6) patient empowerment for hand hygiene; and (7) sound alert for hand hygiene in the patient room’s door. Results: After the insertion of Ozires in 3 ICUs of hospital A (pilot study), the hand hygiene (HH) rate increased from ~36%, between January and July 2016, to ~68% between August 2016 and October 2019. At hospital B, Ozires started his lectures in May 2018, throughout the hospital. Hand hygiene adherence increased from 23% between July and December 2017 to 60% between June 2018 and October 2019. In the 3 months before this multimodal strategy was implemented in hospital C (June–August 2019), and the mean rate of hand hygiene was 65%. With the robot, the hand hygiene rate increased to 94% (September–October 2019). Conclusions: The multimodal strategy centered around the robot Ozires works! Hand hygiene compliance increased significantly after the interventions. People listen the robot much more attentively than to their human colleagues, and healthcare worker behavior changed! We need to go further improve the program, but it is sustainable. Finally, we succeeded in convincing people to improve their hand hygiene practices.Funding: NoneDisclosures: None

Design and Implementation of Smart Glass with Voice Detection Capability to Help Visually Impaired People

The present innovation has wearable device answers for ease the issues of handicapped people. Student-GLASS is a wearable smart camera device built with a powerful microcontroller that has the ability to see what we, normal people, are seeing, understands the user voice requests and supplies the relevant information using auditory feedback through an earphone. The device aims to improve the quality of life for the blind and visually impaired people and makes them understand their surroundings in a clear way as close as to a normal person at an affordable cost. The device consists of an OV7670 camera sensor based vision module, which can capture up to VGA resolution pictures. The device depends on the services of a smart phone for its voice recognition capabilities. A special voice recognition app must be installed on the smart phone which will communicate with the device using a Bluetooth connection. The device supports memory card up to 4GB, to store the necessary audio and image files. It is capable of generating high quality MP3 audio using VS1011e, a DSP based audio codec chip. The user can interact with the device using a four input capacitive touch buttons built into it. This is helpful when the user doesn’t have a smart phone or if its data connectivity is lost.

Flexible and wearable electronic silk fabrics for human physiological monitoring

The development of textile-based devices for human physiological monitoring has attracted tremendous interest in recent years. However, flexible physiological sensing elements based on silk fabrics have not been realized. In this paper, ZnO nanorod arrays are grown in situ on reduced graphene oxide-coated silk fabrics via a facile electro-deposition method for the fabrication of silk-fabric-based mechanical sensing devices. The data show that well-aligned ZnO nanorods with hexagonal wurtzite crystalline structures are synthesized on the conductive silk fabric surface. After magnetron sputtering of gold electrodes, silk-fabric-based devices are produced and applied to detect periodic bending and twisting. Based on the electric signals, the deformation and release processes can be easily differentiated. Human arterial pulse and respiration can also be real-time monitored to calculate the pulse rate and respiration frequency, respectively. Throat vibrations during coughing and singing are detected to demonstrate the voice recognition capability. This work may not only help develop silk-fabric-based mechanical sensing elements for potential applications in clinical diagnosis, daily healthcare monitoring and voice recognition, but also provide a versatile method for fabricating textile-based flexible electronic devices.

Evaluation of Google’s Voice Recognition and Sentence Classification for Health Care Applications

This study examined the use of voice recognition technology in perioperative services (Periop) to enable Periop staff to record workflow milestones using mobile technology. The use of mobile technology to improve patient flow and quality of care could be facilitated if such voice recognition technology could be made robust. The goal of this experiment was to allow the Periop staff to provide care without being interrupted with data entry and querying tasks. However, the results are generalizable to other situations where an engineering manager attempts to improve communication performance using mobile technology. This study enhanced Google’s voice recognition capability by using post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy). The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual’s voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that engineering managers could significantly enhance Google’s voice recognition technology by using post-processing techniques, which would facilitate its use in health care and other applications.

Typed versus voice recognition for data entry in electronic health records: emergency physician time use and interruptions.

IntroductionUse of electronic health record (EHR) systems can place a considerable data entry burden upon the emergency department (ED) physician. Voice recognition data entry has been proposed as one mechanism to mitigate some of this burden; however, no reports are available specifically comparing emergency physician (EP) time use or number of interruptions between typed and voice recognition data entry-based EHRs. We designed this study to compare physician time use and interruptions between an EHR system using typed data entry versus an EHR with voice recognition.MethodsWe collected prospective observational data at 2 academic teaching hospital EDs, one using an EHR with typed data entry and the other with voice recognition capabilities. Independent raters observed EP activities during regular shifts. Tasks each physician performed were noted and logged in 30 second intervals. We compared time allocated to charting, direct patient care, and change in tasks leading to interruptions between sites.ResultsWe logged 4,140 minutes of observation for this study. We detected no statistically significant differences in the time spent by EPs charting (29.4% typed; 27.5% voice) or the time allocated to direct patient care (30.7%; 30.8%). Significantly more interruptions per hour were seen with typed data entry versus voice recognition data entry (5.33 vs. 3.47; p=0.0165).ConclusionThe use of a voice recognition data entry system versus typed data entry did not appear to alter the amount of time physicians spend charting or performing direct patient care in an ED setting. However, we did observe a lower number of workflow interruptions with the voice recognition data entry EHR. Additional research is needed to further evaluate the data entry burden in the ED and examine alternative mechanisms for chart entry as EHR systems continue to evolve.

음성패턴인식 인터랙티브 콘텐츠 개발

언어 학습 콘텐츠에서 공통적으로 가질 수 있는 문제점들을 분석하고 문제점에 대하여 음성 패턴인식기술을 적용하여 기존의 문제점을 해결하였다. 언어 학습 콘텐츠의 첫 번째 문제점은 온라인 학습 자세이다. 수업 진행은 되었지만 다른 웹 페이지를 열어 게임을 하는 등 학생들의 집중력은 떨어졌다. 두 번 째 문제점은 Speaking 학습 과정을 만들었지만 실제로 따라 읽는지 판단할 수가 없었다. 세 번 째 문제점은 학습 관리 시스템에 의한 기계적 진행이 아니라 선생님들의 평가에 의해 잘하는 학생들과 못하는 학생간의 학습 진행에 차이를 둘 필요가 생겼다. 마지막으로 가장 큰 문제는 기존에 만들어 놓은 콘텐츠들은 그대로 유지되면서 위의 문제들을 해결할 수 있어야 했다. 이러한 배경 하에 음성 패턴인식기술은 말하기 학습 전용 학습 프로그램으로 학습 진행을 위한 음성인식은 물론 학습 자체를 위한 음성인식 기능들을 모두 가지고 있으며 인식 절차에 사용된 학습자의 발화 데이터를 원하는 형태의 오디오 파일로 변경하여 서버의 특정 위치로 전송하거나 SQL서버에 등록할 수도 있으며, 또한 컴포넌트이기 때문에 그 어떠한 시스템이나 프로그램이라도 모두 적용 가능하고 이미 만들어진 콘텐츠 전체를 손상시키지 않고 쉽게 삽입하여 새로운 기능들을 사용할 수 있었다. 본 논문으로 교육 방식을 보다 인터렉티브하게 바꾸어 적극적인 수업참여가 되도록 기여하였다. Voice pattern recognition technology to solve the problems of the existing problems and common issues that you may have in language learning content analysis. This is the first problem of language-learning content, online learning posture. Game open another web page through the lesson, but the concentration of the students fell. Have not been able to determine the second issue according Speaking has made the learning process actually reads. Third got a problem with the mechanical process by a learning management system, as well by the teacher in the evaluation of students and students who are learning progress between the difference in the two. Finally, the biggest problem, while maintaining their existing content made to be able to solve the above problem. Speaking learning dedicated learning programs under this background, voice pattern recognition technology learning process for speech recognition and voice recognition capabilities for learning itself has been used in the recognition process the data of the learner's utterance as an audio file of the desired change to a transfer to a specific location of the server or SQL server may be easily inserted into any system or program, any and all applicable content that has already been created without damaging the entire component because the new features were available. Contributed to this paper, active participation in class more interactive teaching methods to change.

A World of “Slippy Maps”: Google Earth, Global Visions, and Topographies of Memory

Having found an area of the planet she is interested in exploring, she takes the equivalent of a ‘magic carpet ride’ through a 3-D visualization of the terrain. Of course, terrain is only one of the many kinds of data with which she can interact. Using the system’s voice recognition capabilities, she is able to request information on land cover, distribution of plant and animal species, real-time weather, roads, political boundaries, and population. She can also visualize the environmental in...

The Use of Voice and Head-Tracking for Hands-Free Human-Computer Interaction

This Practitioner Paper discusses the second in a series of studies examining the Voice Head Integrated Control (VHIC) suite, which combines use of voice commands and a head-tracking device as an alternative to the mouse and keyboard. This VHIC system is currently being investigated as a hands-free interface for accessing digitized aircraft technical manuals. The present study was conducted to validate the voice command vocabulary and provide an initial evaluation of the VHIC hardware. Aircraft maintainers from the 445th C-141 Air Force Reserve Unit served as subjects. The current study made use of the fully functioning VHIC system, complete with head-tracking and voice recognition capabilities. User strategies followed on-screen interface characteristics and the user's experience level with computers. Several error categories were tracked and subjective feedback was collected. A series of recommendations are presented which address the strategic application of the VHIC system in an aircraft maintenance environment.

The Brave New World of the Cyber Speech and Hearing Clinic

The Brave New World of the Cyber Speech and Hearing Clinic

Telecommunication system tester with voice recognition capability

Connection is made directly or via a switched telephone network between a tester and a telecommunication system to be tested. User data, such as voice signals, and signalling data, such as DTMF signals are generated by the tester to cause the telecommunication system to respond. The user and signalling data may be generated interactively to test the telecommunication system in real time, or by executing prerecorded scripts. When the tester is used interactively, the representations of the signals transmitted to and from the tester are recorded in at least one script file. Voice recognition is preferably used to determine whether the response received from the telecommunication system is the expected response, particularly when a script is executed. The vocabulary used for voice recognition may be created by repeatedly executing a script file (recorded during interactive operation of the tester or created manually) which causes the telecommunication system to generate the desired prompt(s) and message(s). Alternatively, the vocabulary may be obtained in other ways, such as by reproducing voice signals at the telecommunication system.

Oral diagnostic reporting and synchronized image filing using magneto-optical disks

An experimental radiologic reporting system has been developed and tested. The rewritable and compact magneto-optical disk (MOD) is applied to storing medical images with oral diagnostic reports of these radiologic images. The disk is 5.25 inches in diameter, has 600 MB memory capacity, is erasable, light and compact. Advantages are simultaneous recording of radiologic images and their oral reports by radiologists, and application to circulation of media inside the hospital as well as to filing of medical images. The MOD has a multimedia function of communication and filing. When medical images are taken and stored, oral interpretation by radiologists can be simultaneously added. Physicians can get information of the images and their reports by oral speech at the same time in front of computer workstation. Furthermore, integration of a voice recognition capability is now being undertaken.

Frequency and Time-Domain Differentiation of Speech Contour Data for Voice Recognition

In an attempt to enhance voice recognition capabilities, several transformations have been applied to the speech acoustical signal. The transformations include first- and second-order partial differentiation of the amplitude of the frequency-time-amplitude contour with respect to frequency and with respect to time, and variable segmentation of this contour and the derivative contours based upon criteria obtained from the partial derivative information. To provide the initial data base for the experiments, an instrumentation system was implemented to digitize the speech contours with the high degree of resolution of 256 frequency channels and 125 time increments or 32 000 data points. Computer-generated contour spectrograms were produced for the original contour and for the first and second partial-derivative contours. Speech recognition and speaker identification systems have been computer simulated to evaluate the effectiveness of the transformations.