Knowledge Of Sign Language Research Articles

STATIC SIGN LANGUAGE TRANSLATOR USING HAND GESTURE AND SPEECH RECOGNITION

Communication between ordinary and deaf people often has issues because ordinary people lack knowledge of sign language. This research aims to help ordinary people communicate with hearing impaired (deaf) people using sign language. This research aims to produce an Android-based mobile app that can translate static sign language using hand movements into text and also convert the spoken voice into sign language using speech recognition. The framework in this research for hand gesture detection uses the MediaPipe software program. This framework allows the creation of apps that translate hand movements into text that help ordinary people understand sign language. Speech recognition in this research uses the Android speech library. This research succeeded in detecting static letters of the alphabet from the a-z and numbers 0-9. Tests of 540 hand gestures carried out in the morning, afternoon, evening, and night had an average detection time of 4.37 seconds. The fastest object detection times were in the morning at a distance of 30 cm with an average detection time of 2.5 seconds. Based on acceptance testing, 83.13% of the features in this static sign language translator have met the users ’users’ needs when communicating with the deaf using sign language.

Derived Amharic alphabet sign language recognition using machine learning methods

Hearing-impaired people use sign language as a means of communication with those with no hearing disability. It is therefore difficult to communicate with hearing impaired people without the expertise of a signer or knowledge of sign language. As a result, technologies that understands sign language are required to bridge the communication gap between those that have hearing impairments and those that dont. Ethiopian Amharic alphabets sign language (EAMASL) is different from other countries sign languages because Amharic Language is spoken in Ethiopia and has a number of complex alphabets. Presently in Ethiopia, just a few studies on AMASL have been conducted. Previous works, on the other hand, only worked on basic and a few derived Amharic alphabet signs. To solve this challenge, in this paper, we propose Machine Learning techniques such as Support Vector Machine (SVM) with Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG), and their hybrid features to recognize the remaining derived Amharic alphabet signs. Because CNN is good for rotation and translation of signs, and HOG works well for low quality data under strong illumination variation and a small quantity of training data, the two have been combined for feature extraction. CNN (Softmax) was utilized as a classifier for normalized hybrid features in addition to SVM. SVM model using CNN, HOG, normalized, and non-normalized hybrid feature vectors achieved an accuracy of 89.02%, 95.42%, 97.40%, and 93.61% using 10-fold cross validation, respectively. With the normalized hybrid features, the other classifier, CNN (sofmax), produced a 93.55% accuracy.

A CASE STUDY ON TEACHING METHODS USED TO TEACH LEARNERS WITH HEARING IMPAIRMENT AT BUBUBU PRIMARY SCHOOLS IN ZANZIBAR.

Background: This study aimed to investigate the teaching methods used to teach learners with hearing impairment at, Mtopepo, Chuini, Kama, and Kidichi primary schools of Bububu ward in Zanzibar. Methodology: A descriptive case study research was conducted in which Qualitative data was collected from 36 members who included 25 learners 25 ordinary teachers, 5 Academic masters, and 5 head teachers from the selected schools. Results: The majority of respondents were Females 40 (66.6%) and 20 (33.3%) were males. 30 (50%) had primary education. 10 (16.6%) certificate education and 20 (33.3%) diploma education. By age, 58.3% of the respondents were 5 -15 years, 16.6% were 16 – 35 years and 25% were 36-55 years. The themes of the study were Headteacher understanding of the academic master lesson, Ordinary teacher-appropriate teaching methods, and Learner contribution in the class. Most teachers use a general approach to the students in a mixed class without considering that some students with special needs require a particular approach to understand the lesson. Some teachers were doing recommended teaching approaches to the students with special needs and their students were performing well, sometimes well compared to other normal students. Some teachers found it uneasy to teach students with special needs and lacked sufficient teaching knowledge, special tools, and materials like sign language knowledge which they also did not understand. Conclusion: There was a shortage of special needs teachers for hearing impairment in all the selected schools. The schools lacked enough special needs teachers who could be used to teach inclusive classes for hearing-impaired learners. Recommendation: The Ministry of Education Vocational Training of Zanzibar should improve teaching and learning methods for learners with hearing impairment in primary schools of Zanzibar.

Indian Sign Language Recognition using transfer learning with Efficient Net

ABSTRACT Automatic conversion of sign language into text or speech can be really useful in communicating among deaf or mute people with no sign language knowledge. The present need is to create an automated system that can translate ISL signals into regular text and vice versa. It is advantageous for both groups to communicate their needs to one another while using publicly accessible services, such as banking, traffic signaling, and ticketing. In this manuscript, Indian Sign Language Recognition using Transfer Learning with Efficient Net (ISLR-EfficientNetB0) is proposed for Indian sign language recognition. Initially, the input images are gathered via the Indian Sign Language (ISL) dataset. The input image is pre-processing by Fairness-aware Collaborative Filtering (FACF) method. In pre-processing stage noise are removed from input images and image resizing is done. Then, Efficient Net B0 is employed to identify ISL. The proposed model is implemented on Python. Then the efficiency of the proposed approach is compared with existing models. The accuracy, precision, specificity, sensitivity, F1-score, computational time, error rate and RoC of the suggested approaches are all assessed. The proposed ISLR-EfficientNetB0 method attains 31.42%, 26.04%, and 20.45% higher accuracy, 19.24%, 26.35%, and 24.02% higher precision, 15.84%, 18.91%, and 28.16% higher specificity and 17.06%, 21.36%, and 32.47% higher sensitivity compared with existing methods, such as Indian sign language recognition under machine learning methods (ISLR-KNN), ISL recognition scheme utilizing SURF with SVM and CNN (ISLR-SVM-CNN) and Benchmarking deep neural network methods for ISL recognition (ISLR-VGG16) respectively.

Using LSTM to translate Thai sign language to text in real time

Between 2019 and 2022, as the Covid-19 pandemic unfolded, numerous countries implemented lockdown policies, leading most corporate companies to permit employees to work from home. Communication and meetings transitioned to online platforms, replacing face-to-face interactions. This shift posed challenges for deaf or hearing-impaired individuals who rely on sign language, using hand gestures for communication. However, it also affected those who can hear clearly but lack knowledge of sign language. Unfortunately, many online meeting platforms lack sign language translation features. This study addresses this issue, focusing on Thai sign language. The objective is to develop a model capable of translating Thai sign language in real-time. The Long Short-Term Memory (LSTM) architecture is employed in conjunction with MediaPipe Holistic for data collection. MediaPipe Holistic captures keypoints of hand, pose, and head, while the LSTM model translates hand gestures into a sequence of words. The model’s efficiency is assessed based on accuracy, with real-time testing achieving an 86% accuracy, slightly lower than the performance on the test dataset. Nonetheless, there is room for improvement, such as expanding the dataset by collecting data from diverse individuals, employing data augmentation techniques, and incorporating an attention mechanism to enhance model accuracy.

Revealing sign language for individuals who are deaf and mute through the utilization of neural networks

Living without effective communication poses significant challenges for humans. Individuals employ various methods to convey and share their thoughts and ideas between the sender and receiver. Two of the most prevalent means of communication are verbal speech, which relies on auditory perception, and non-verbal communication through gestures involving bodily movements such as hand gestures and facial expressions. Sign language, specifically categorized as a gestural language, is a unique form of communication that relies on visual perception for understanding and expression. While many individuals incorporate gestures into their communication, for deaf individuals, sign language is often their primary and essential means of communication. Individuals who are deaf and dumb rely on communication to interact with others, gain knowledge, and engage in their surroundings. Sign language serves as a crucial link that reduces the distance between them and the broader society. In order to enhance this communication, we've created models with the ability to identify sign language gestures and translate them into conventional text. Through the training of these models on a dataset employing neural networks, remarkable outcomes have been attained. This technology enables individuals, without prior knowledge of sign language, to understand the intentions and messages of individuals with disabilities, fostering greater inclusivity and accessibility in our society. Three algorithms were used to achieve this work and the findings show very good outcomes, i.e. Random Forest at 98%, Logistic Regression at 99%, and Decision Tree at 91%. Keywords: Machine Learning, Sign Language, Random Forest, Neural Network, Logistic Regression Deaf and Dumb, Decision Tree

SIGN LANGUAGE OF THE DEAF PEOPLE: A STUDY ON PUBLIC UNDERSTANDING

Today, when various regulatory documents support sign language research, there is a lack of both teaching materials and research on deaf sign language and public understanding. The study aims to determine the public’s proficiency or competence in the Latvian sign language. The research revealed people’s basic knowledge of Latvian sign language grammar, their ability to communicate and understand deaf people, their attitudes and opinions about the process, and the necessity of learning sign language. Information was studied in four blocks of questions: society’s attitude towards deaf sign language, society’s knowledge of deaf sign language, communication skills with a deaf person, and opinion on the development of deaf sign language and its necessity. The sample of the study is 500 respondents, who represent the opinion of the population between the ages of 16 and 60, observing the socio-demographic parameters of the target group of the study. An empirical research method was used in the study; to collect data, an electronic survey was conducted. In general, the study confirms that society shows a positive attitude towards Latvian sign language as a means of communication for deaf people. At the same time, the data reveal poor knowledge of sign language and its grammar. It was concluded that not only sociological research is needed on public perceptions of deaf sign language, but also various educational events for the rest of society should be promoted, including seminars, courses, and conferences.

Satisfaction with life in a sample of prelingually deaf cochlear implant users with a good command of spoken Polish as the primary language

IntroductionThis study investigated the level of satisfaction with life (SWL) in a group of cochlear implant (CI) users who had been prelingually deaf but were orally educated. They had received one or two CIs (as a child, adolescent, or adult) and were highly competent Polish speakers. This study looked at three factors that may affect SWL – psychosocial, deafness/hearing and communication related, and sociodemographic. MethodsThe participants were prelingually deaf CI users who had learned highly competent spoken Polish as their primary language. They had been educated in mainstream or integrated schools (not schools for the deaf), and had no other disability or severe illness. Measurements were done with 5 questionnaires: the Satisfaction With Life Scale (SWLS), the I–Others Questionnaire, the Patient Health Questionnaire (PHQ-9), the Deaf Identity Development Scale (DIDS), and the Nijmegen Cochlear Implant Questionnaire (NCIQ). ResultsThe SWL level of the group was similar to that of the standard Polish population. SWL was positively related to positive self-perception, acceptance of oneself as a deaf person, and to perceiving the benefits of having a CI (as measured by three NCIQ domains: self-esteem, activity limitations, and social interactions). On the other hand, negative self-perception, marginal deaf identity, and depressive symptoms were negatively related to SWL. There was no relationship between SWL and knowledge of sign language. Lower depressive symptoms and greater hearing loss were both significant predictors of SWL, although those who used two CIs generally had a lower SWL. ConclusionsPrelingually deaf CI users with low SWL require psychological support in many spheres, including working through problems of deaf identity, self-acceptance, and depression. Additional research should involve diverse DHH CI users, including those with limited spoken Polish competency or sign language skills, as well as members of the Polish Deaf community.

Health-related quality of life and knowledge of sign language as a second language in adults with prelingual deafness using cochlear implant

The article aims to find an answer to the question whether the knowledge of sign language in the subjective assessment of study participants is connected to health-related quality of life (HRQOL) specific in terms of deafness/hearing and life satisfaction. The study involved 107 people with prelingual deafness using cochlear implants since childhood, adolescence, or adulthood. The results show significant relationship between the quality of life related to deafness/hearing assessed using the Nijmegen Cochlear Implant Questionnaire and the level of knowledge of sign language the following areas: advanced auditory perception and speech perception. Participants fluent in sign language obtained significantly lower results in these areas. Moreover, it was fund that the proportion of people who do not use telephones was larger in the group with a high level of knowledge of sign language than in the groups with medium or no knowledge of sign language. However, there were no significant difference, due to knowledge of sign language, in relation to life satisfaction measured using the Satisfaction with Life Scale. The discussed issue of the role of sign language in relation to the HRQOL in people with prelingual deafness requires further research, including, among others, objective assessment of linguistic competence in Polish Sign Language and the age and method of their acquisition, as well as qualitative research.

Exploring critical factors affecting sign language knowledge and motivation for sign language learning in nurses: A cross-sectional study

Introduction: In times when the term inclusion, rights of patients, and the patient as a subject in health care are used more often, the limited availability of written material or sign language interpreters at health services is still a key barrier to health services for people who are hearing-impaired. The aim of this study is to examine nurses’ knowledge of communication skills with hearing-impaired patients, their preferred methods, and the possibility of using translation services. Methods: The study was cross-sectional, and data were collected in September–October 2019. The study included 407 nursing students. A demographic data form and questions divided into three parts, namely, communication skills (six questions), communication methods (three questions), and interpretation services (five questions), were used to collect data. Data were analyzed with descriptive statistics. Results: The questionnaire was administered among 424 students, and total of 407 students completed the questionnaire. Most of the respondents were female (320 [78.6%]) with 0–5 years of work experience (227 [55.8%]) in the tertiary level of healthcare (184 [45.3%]). The results showed that 326 (80.1%) of the respondents encountered a person with some form of hearing impairment during their work. Sign language was used by 56 (13.8%) of the respondents, but 74 (18.14%) nurses did not communicate when meeting with hearing-impaired patients. Two-thirds of the respondents never had the opportunity to learn sign language, and 43 (10.54%) respondents would choose an official interpreter as support. Male respondents were aware of the importance of communication with hearing-impaired patients and the use of an application for pain assessment (p < 0.05). No statistically significant difference was observed regarding the department in which the respondents work (p > 0.05). Conclusion: The results of the study showed insufficient knowledge and skills of nurses to communicate with hearing-impaired people.

SIGN RECOGNITION AND VOICE CONVERSION DEVICE FOR DUMB

At most of us are blessed with natural capability to see, hear and interact. Still, numerous people do not have this capability. Due to various reasons like birth scars, accidents, oral conditions, and environmental impacts the number of analogous people is swiftly adding in the recent times. Ultimate of the deaf and dumb use gesture and subscribe symbol for the communication. Subscribe language is the form of communication where shapes, arms or body, exposure and movement of hands, and facial expressions are completely combined. The communication gap between two persons is because of a dumb person uses sign language which is not comprehensible by a normal person. This design mainly focuses on removing the barricade of communication between the mute community and the people not familiar with the generality of sign language so that the dispatches that a dumb person is trying to bear is accessible to a person with no knowledge of sign language. The design of the device is predicated on bedded systems. Keywords Arduino, Dumb, Bedded systems, Gesture recognition, Speech recognition, Speech processing.

QMX-BdSL49: An Efficient Recognition Approach for Bengali Sign Language with Quantize Modified Xception

Sign language is developed to bridge the com-munication gap between individuals with and without hearing impairment or speech difficulties. Individuals with hearing and speech impairment typically rely on hand signs as a means of expressing themselves. However, people, in general, may not have sufficient knowledge of sign language, thus a sign language recognition system on an embedded device is most needed. Literature related to such systems on embedded devices is scarce as these recognition tasks are very complex and computationally expensive. The limited resources of embedded devices cannot execute complex algorithms like Convolutional Neural Network (CNN) properly. Therefore, in this paper, we propose a novel deep learning architecture based on default Xception architec-ture, named Quantized Modified Xception (QMX) to reduce the model’s size and enhance the computational speed without compromising model accuracy. Moreover, the proposed QMX model is highly optimized due to the weight compression of model quantization. As a result, the footprint of the proposed QMX model is 11 times smaller than the Modified Xception (MX) model. To train the model, BDSL 49 dataset is utilized which includes approximately 14,700 images divided into 49 classes. The proposed QMX model achieves an overall F1 accuracy of 98%. In addition, a comprehensive analysis among QMX, Modified Xception Tiny (MXT), MX, and the default Xception model is provided in this research. Finally, the model has been implemented on Raspberry Pi 4 and a detailed evaluation of its performance has been conducted, including a comparison with existing state-of-the-art approaches in this domain. The results demonstrate that the proposed QMX model outperforms the prior work in terms of performance.

Sustainable Hand Gesture Recognition for Speech Conversion, Empowering the Speech-Impaired

A sustainable language disorder affects an individual’s ability to reach out to others through speaking and listening. So utilizing sustainable hand gestures is among the most widespread means of non-verbal and visual communication used by people with speech disabilities worldwide. However, even though sustainable sign language is used everywhere by speech-impaired and hearing-impaired people, most of the populace who don't have any knowledge about sign language face difficulties in sustainably communicating with them. This sustainable problem requires better solutions that can successfully support communication for people with speech disabilities. This sustainable approach will reduce the communication gap for the speech-impaired population. There are many sustainable solutions in the market such as using sensors to make a sustainable device that gives a helpful output. But these sustainable solutions are expensive and not everyone can afford them. We are employing Convolutional Neural Networks to create a sustainable model that is trained on different gestures. This sustainable model enables speech-impaired individuals to convey their information using signs which get converted to human-understandable language, and sustainable voice is given as output. The sustainable hand gestures made are captured as a series of sustainable images which are processed using Python code. This sustainable endeavor introduces a solution that not only automates the identification of sustainable hand gestures but also transforms them into sustainable speech. By interpreting these recognized sustainable gestures, the corresponding recorded audio will be played sustainably. The focus of this sustainable paper is to offer accessibility, convenience, and safety to individuals with speech impairments in a sustainable manner. These sustainable individuals often experience societal discrimination solely due to their disabilities. This sustainable paper is aimed at innovating a sustainable device to help those without the knowledge of sign language sustainably communicate with the people who face difficulty in speech.

Teacher Proficiency in Sign Language and Reading Skills Development of Learners with Hearing Impairment

This study examined teachers’ proficiency in Sign Language and the methods employed to improve the reading abilities among learners with Hearing Impairments. Qualitative method was applied through the use of in-depth interviews, classroom observations and focus group discussions. The study comprised 115 participants who were purposively sampled as follows: Twenty-six (26) teachers of students with Hearing Impairment, sixteen (16) administrators thirteen (13) parents of students with Hearing Impairment, and sixty (60) grade three (3) students out of which a total of thirty (30) Hearing Impairments whereas the other thirty had no Hearing Impairments. The study found that teachers lacked knowledge of sign language. Although the Primary Literacy Programme incorporates systematic explicit instructions, teachers were not able to adequately apply this evidence based instructional strategies when it came to teaching learners with Hearing Impairments due to lack of proficiency in Sign Language. Given that the phonic methodology’s strength rests in the use of sounds, it was observed that instructional strategies were not utilised when teaching children with Hearing Impairment. Teachers also had limited opportunities for Continuous Professional Development to familiarise themselves with the PLP methodologies and hone their Sign Language skills. The study recommends improved pre-service teacher training in Sign Language as well as increased opportunities for Continuous Professional Development among in-service teachers.

Deaf people and sign language: an enigma in dentistry

Sign language is the main means of communication for deaf people; consists of the combination of manual, body and facial movements with specific meanings. Among the main barriers faced by this community are the lack of communication, shortage of health services, added to the inexperience and lack of knowledge of sign language by the health professional. This article aims to raise awareness of the importance and need of sign language in dental clinical care and professional training, as well as strategies, tools and recommendations for better care for deaf people. Databases such as SciELO, Google Scholar, PubMed and ScienceDirect were included during the years 2007 and 2021 It was concluded that the main barriers during communication are the little knowledge of sign language in dentists, the little access to interpreters and difficulties in obtaining appointments; affecting the susceptibility to develop oral diseases. The inclusion of sign language courses within the university curriculum provides academic and professional ethics benefits; however, follow-up is necessary to confirm compliance with the requested parameters. Among the main tools and strategies, the use of educational videos, the accompaniment of interpreters and, in view of technological development, the use of mobile applications, can facilitate communication. Likewise, a set of recommendations is proposed for the approach of adult and child deaf patients.

Movement-in-a-Video Detection Scheme for Sign Language Gesture Recognition Using Neural Network

Sign language aids in overcoming the communication barrier between hearing-impaired individuals and those with normal hearing. However, not all individuals with normal hearing are skilled at using sign language. Consequently, deaf and hearing-impaired individuals generally encounter the problem of limited communication while interacting with individuals with normal hearing. In this study, a sign language recognition method based on a movement-in-a-video detection scheme is proposed. The proposed scheme is applied to extract unique spatial and temporal features from each gesture. The extracted features are subsequently used to train a neural network to classify the gestures. The proposed movement-in-a-video detection scheme is applied to sign language videos featuring short, medium, and long gestures. The proposed method achieved an accuracy of 90.33% and 40% in classifying short and medium gestures, respectively, compared with 69% and 43.7% achieved using other methods. In addition, the improved accuracies were achieved with less computational complexity and cost. It is anticipated that improvements in the proposed method, for it to achieve high accuracy for long gestures, can enable hearing-impaired individuals to communicate with normal-hearing people who do not have knowledge of sign language.

SignUpCrowd: Using Sign-Language as an Input Modality for Microtask Crowdsourcing

Different input modalities have been proposed and employed in technological landscapes like microtask crowdsourcing. However, sign language remains an input modality that has received little attention. Despite the fact that thousands of people around the world primarily use sign language, very little has been done to include them in such technological landscapes. We aim to address this gap and take a step towards the inclusion of deaf and mute people in microtask crowdsourcing. We first identify various microtasks which can be adapted to use sign language as input, while elucidating the challenges it introduces. We built a system called ‘SignUpCrowd’ that can be used to support sign language input for microtask crowdsourcing. We carried out a between-subjects study (N=240) to understand the effectiveness of sign language as an input modality for microtask crowdsourcing in comparison to prevalent textual and click input modalities. We explored this through the lens of visual question answering and sentiment analysis tasks by recruiting workers from the Prolific crowdsourcing platform. Our results indicate that sign language as an input modality in microtask crowdsourcing is comparable to the prevalent standards of using text and click input. Although people with no knowledge of sign language found it difficult to use, this input modality has the potential to broaden participation in crowd work. We highlight evidence suggesting the scope for sign language as a viable input type for microtask crowdsourcing. Our findings pave the way for further research to introduce sign language in real-world applications and create an inclusive technological landscape that more people can benefit from.

How do face masks impact communication amongst deaf/HoH people?

Face coverings have been key in reducing the spread of COVID-19. At the same time, they have hindered interpersonal communication, particularly for those who rely on speechreading to aid communication. The available research indicated that deaf/hard of hearing (HoH) people experienced great difficulty communicating with people wearing masks and negative effects on wellbeing. Here we extended these findings by exploring which factors predict deaf/HoH people’s communication difficulties, loss of information, and wellbeing. We also explored the factors predicting perceived usefulness of transparent face coverings and alternative ways of communicating. We report the findings from an accessible survey study, released in two written and three signed languages. Responses from 395 deaf/HoH UK and Spanish residents were collected online at a time when masks were mandatory. We investigated whether onset and level of deafness, knowledge of sign language, speechreading fluency, and country of residence predicted communication difficulties, wellbeing, and degree to which transparent face coverings were considered useful. Overall, deaf/HoH people and their relatives used masks most of the time despite greater communication difficulties. Late-onset deaf people were the group that experienced more difficulties in communication, and also reported lower wellbeing. However, both early- and late-onset deaf people reported missing more information and feeling more disconnected from society than HoH people. Finally, signers valued transparent face shields more positively than non-signers. The latter suggests that, while seeing the lips is positive to everyone, signers appreciate seeing the whole facial expression. Importantly, our data also revealed the importance of visual communication other than speechreading to facilitate face-to-face interactions.

Using agile methods to improve inclusion and team diversity / Utilização de métodos ágeis para melhorar a inclusão e a diversidade da equipe

Agile Methodologies have brought insights into how to develop software by focusing on individuals and valuing diversity in the teams. Some studies show that diversity builds better teams and delivers better results. However, there is a known lack of representativeness when discussing diversity for people with disabilities. In this context, this paper aims at identifying the leading prac- tices/techniques that allow an open concept of social inclusion and diversity in agile software development teams and propose any necessary adaptations to help wide diversity. We performed a literature review and raised a set of techniques that were presented to experts in two contexts: (i) within the software development industry and working under agile methodologies and (ii) with disabilities or people who coordinate teams with people with disabilities. The results show that some agile techniques can promote inclusion but is necessary for some adaptations. We verify that to encourage this diversity for people with disabilities, specific tools, knowledge of sign language and transformations in the environment is vital for supporting broad diversity.

Design of Double-effect Propulsion System for News Broadcast Based on Artificial Intelligence and Virtual Host Technology

A virtual host refers to a host who uses an avatar to submit contributions on a video website. The virtual host is a powerful combination of artificial intelligence technology and live broadcast. In recent years, virtual video hosts have sprung up on major video websites, and their popularity is growing rapidly. Virtual host technology has been applied to many areas of society. Based on this background, this article designs a news broadcast double-effect propulsion system based on artificial intelligence and virtual host technology. This article applies the virtual host technology to the field of news broadcasting, and realizes the double-effect advancement of virtual host technology and news broadcasting. The virtual host designed in this article mainly uses sign language to broadcast news, so this article first conducts a quick and brief knowledge of sign language, and then conducts a detailed analysis of the current research status of virtual host technology and artificial intelligence technology, and uses artificial intelligence. The technology realizes the motion control of the virtual host; then the intelligent algorithm is used to realize the superimposition and synthesis of videos, and a news broadcast double-effect propulsion system of artificial intelligence and virtual host technology is designed. Finally, this article carried out an experimental test on the system’s simultaneous broadcast function and channel switching function. The test results surface excluded subjective human factors. After the improvement, the system can achieve simultaneous broadcast and successful channel switching.