Effects of Voice-Based Synthetic Assistant on Performance of Emergency Care Provider in Training

Damacharla, Praveen; Dhakal, Parashar; Stumbo, Sebastian; Javaid, Ahmad Y.; Ganapathy, Subhashini; Malek, David A.; Hodge, Douglas C.; Devabhaktuni, Vijay

doi:10.1007/s40593-018-0166-3

Cited by 25 publications

(38 citation statements)

References 36 publications

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“…These units are combined into a pronunciation database. In the process of synthesis, the monosyllabic of the pronunciation database is adjusted, and the splicing algorithm is used to splice the complete sentences, so as to achieve the function of speech output [28][29][30][31].…”

Section: Design Of Ai Voice Interaction System For An Educational Robotmentioning

confidence: 99%

Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

Yang

Wang

2020

Sustainability

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In order to promote the development of individualized, accurate and intelligent physical education teaching, combined with artificial intelligence technology, the current physical education teaching mode has been improved. Through the establishment of an artificial intelligence educational robot based on voice interaction, a hybrid physical education teaching mode is constructed to realize personalized education for students. First, the speech recognition system is designed from three aspects of speech recognition, interaction management and speech synthesis, and the accuracy of recognition is improved by algorithm. Second, a new mode of hybrid physical education teaching is constructed. Through intelligent information technology, the advantages of traditional physical education teaching are combined to improve the classroom efficiency of physical education teaching and personalized education ability for students. Finally, the relevant experimental scheme and questionnaire are designed, and the actual situation of an educational robot introduced into physical education teaching is investigated and evaluated. The results show that the recognition accuracy of the artificial intelligence speech recognition system can reach more than 90%. It can communicate well with students and answer students’ questions. An educational robot is introduced into physical education teaching, and students’ learning attitude and interest are evaluated. The results show that before and after the introduction of an educational robot in physical education teaching, the average score of students’ learning interest increases by 21 points, and the average score of learning attitude increases by 9.8 points. Therefore, the introduction of an artificial intelligence educational robot based on voice interaction in physical education teaching can help to improve the classroom efficiency of physical education teaching and students’ interest. This study provides a reference for the development of artificial intelligence teaching and promoting the development of artificial intelligence.

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Section: Design Of Ai Voice Interaction System For An Educational Robotmentioning

confidence: 99%

Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

Yang

Wang

2020

Sustainability

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“…The developed SA technology is integrated into Amazon Web Services (AWS) with respect to language features and micro-services available. [8] states that Alexa Voice Services (AVS) uses both NLP and SS and can be accurately trained for different accents. The database was developed and stored in Amazon DynamoDB, and the primary operation blocks (scenario calculator, process monitor, result synthesizer) were developed in AWS Lambda.…”

Section: Vbsa Designmentioning

confidence: 99%

“…A detailed discussion of the tracking process has been discussed in our previous paper [8]. The detailed version of the VBSA architecture and development process has been inspired by our previous research on HMT [8,[45][46][47]. Figure 4 presents modes of operation of the VBSA, while Figure 5 presents the UML diagram of the VBSA architecture depicting the AWS components that were used to develop the cloud-based IA.…”

Section: Vbsa Designmentioning

confidence: 99%

“…Indeed the VBSA can execute all the modes of operations highlighted in Figure 4 collectively with an option to disable either of them if needed. On the other hand, a observer uses a web-based dashboard, as shown in [8] fig. 4, to monitor the training progress of all the trainees, whereas the administrative staff uses a web-based form as an interface to build new training scenarios.…”

Section: Vbsa Designmentioning

confidence: 99%

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Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human-Machine Teams (HMT)

Damacharla¹,

Dhakal²,

Bandreddi³

et al. 2020

Preprint

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This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

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“…It is notable that limited response time, lack of standard technical language/protocol, and the difference in knowledge of equipment among the caretakers, serve as primary sources of these errors. It is expected that the use of a speaker recognition system integrated with an existing Natural Language Processing (NLP) system for voice-activated decision making or synthetic assistance (SA), might reduce such errors to some extent, and improve the voice-based UI while providing secure access to these systems [3,4]. Such systems, involving the use of synthetic assistants (SA) or voice-activated decision-making, would also require authentication through speaker recognition.…”

Section: Introductionmentioning

confidence: 99%

A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface

Dhakal

Damacharla

Javaid

et al. 2019

MAKE

Self Cite

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In this paper, we present a novel pipelined near real-time speaker recognition architecture that enhances the performance of speaker recognition by exploiting the advantages of hybrid feature extraction techniques that contain the features of Gabor Filter (GF), Convolution Neural Networks (CNN), and statistical parameters as a single matrix set. This architecture has been developed to enable secure access to a voice-based user interface (UI) by enabling speaker-based authentication and integration with an existing Natural Language Processing (NLP) system. Gaining secure access to existing NLP systems also served as motivation. Initially, we identify challenges related to real-time speaker recognition and highlight the recent research in the field. Further, we analyze the functional requirements of a speaker recognition system and introduce the mechanisms that can address these requirements through our novel architecture. Subsequently, the paper discusses the effect of different techniques such as CNN, GF, and statistical parameters in feature extraction. For the classification, standard classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Deep Neural Network (DNN) are investigated. To verify the validity and effectiveness of the proposed architecture, we compared different parameters including accuracy, sensitivity, and specificity with the standard AlexNet architecture.

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Effects of Voice-Based Synthetic Assistant on Performance of Emergency Care Provider in Training

Cited by 25 publications

References 36 publications

Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human-Machine Teams (HMT)

A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface

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