Voice Activity Research Articles

The improvement of oral communicative competence in english through the artificial intelligence

The study "Improvement of Oral Communicative Competence in English through the Artificial Intelligence" focused on exploring how artificial intelligence technology can influence the development of oral communication skills during the teaching and learning process of English as a second language, with the participants of this research being future teachers of the subject. This research aimed to determine the effectiveness of artificial intelligence applications, such as voice assistants and speech recognition systems, in improving students' oral communicative competence. The study examined how these artificial intelligence systems provided instant and personalized feedback, as well as opportunities for continuous oral practice for students. It also explored how artificial intelligence can adapt to the individual needs of students, which was beneficial for those with different skill levels or specific learning needs. The results of this research demonstrated that teaching and learning mediated by artificial intelligence had significant implications, suggesting how artificial intelligence technology can play an integral role in the development of oral communicative competence in English and possibly other languages. Finally, this teaching approach promises to facilitate more effective and accessible learning for a larger number of students, enhancing their oral communication skills in the process.

You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification.

In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan incrementally as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn. The ability to generate iCRs in response to pauses is therefore important in building natural and robust everyday voice assistants (EVA) such as Amazon Alexa. This becomes crucial with people with dementia (PwDs) as a target user group since they are known to pause longer and more frequently, with current state-of-the-art EVAs interrupting them prematurely, leading to frustration and breakdown of the interaction. In this article, we first use two existing corpora of truncated utterances to establish the generation of clarification requests as an effective strategy for recovering from interruptions. We then proceed to report on, analyse, and release SLUICE-CR: a new corpus of 3,000 crowdsourced, human-produced iCRs, the first of its kind. We use this corpus to probe the incremental processing capability of a number of state-of-the-art large language models (LLMs) by evaluating (1) the quality of the model's generated iCRs in response to incomplete questions and (2) the ability of the said LLMs to respond correctly after the users response to the generated iCR. For (1), our experiments show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes and only when prompted with example iCRs from our corpus. For (2), our results are in line with (1), that is, that larger LLMs interpret incremental clarificational exchanges more effectively. Overall, our results indicate that autoregressive language models (LMs) are, in principle, able to both understand and generate language incrementally and that LLMs can be configured to handle speech phenomena more commonly produced by PwDs, mitigating frustration with today's EVAs by improving their accessibility.

Laser-Based Command Injection Attacks on Voice-Controlled Microphone Arrays

Voice-controlled (VC) systems, such as mobile phones and smart speakers, enable users to operate smart devices through voice commands. Previous works (e.g., LightCommands) show that attackers can trigger VC systems to respond to various audio commands by injecting light signals. However, LightCommands only discusses attacks on devices with a single microphone, while new devices typically use microphone arrays with sensor fusion technology for better capturing sound from different distances. By replicating LightCommands’s experiments on the new devices, we find that simply extending the light scope (just as they do) to overlap multiple microphone apertures is inadequate to wake up the device with sensor fusion. Adapting LightCommands’s approach to microphone arrays is challenging due to their requirement for multiple sound amplifiers, and each amplifier requires an independent power driver with unique settings. The number of additional devices increases with the microphone aperture count, significantly increasing the complexity of implementing and deploying the attack equipment. With a growing number of devices adopting sensor fusion to distinguish the sound location, it is essential to propose new approaches to adapting the light injection attacks to these new devices. To address these problems, we propose a lightweight microphone array laser injection solution called LCMA (Laser Commands for Microphone Array), which can use a single laser controller to manipulate multiple laser points and simultaneously target all the apertures of a microphone array and input light waves at different frequencies. Our key design is to propose a new PWM (Pulse Width Modulation) based control signal algorithm that can be implemented on a single MCU and directly control multiple lasers via different PWM output channels. Moreover, LCMA can be remotely configured via BLE (Bluetooth Low Energy). These features allow our solution to be deployed on a drone to covertly attack the targets hidden inside the building. Using LCMA, we successfully attack 29 devices. The experiment results show that LCMA is robust on the newest devices such as the iPhone 15, and the control panel of the Tesla Model Y.

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

Factors influencing older adults' acceptance of voice assistants.

Voice assistants (VAs) have the potential to uphold and enhance the quality of life for older adults. However, the extent to which older adults accept and benefit from VAs may be relatively modest. This study developed a comprehensive model combined with product and personal characteristics to explain the acceptance of VAs among older adults, using semi-structured interviews (Study 1) and questionnaires (Study 2). Results revealed that in terms of product characteristics, perceived usefulness and perceived enjoyment significantly affect behavior intention. Regarding personal characteristics of older adults, technological self-efficacy and dispositional resistance to change significantly affect behavior intention. However, no direct impact of perceived ease of use and perceived trust on behavior intention. Additionally, perceived enjoyment influenced both perceived ease of use and perceived usefulness. Results suggested the significant role of technology self-efficacy and dispositional resistance to change in predicting the acceptance of VAs among older adults. Our newly developed model offers valuable insights for tailoring VAs to this demographic during design and implementation.

Green Transitions in Developing Countries: Perspectives on Women’s Political Leadership

Green transition by reducing CO2 emissions and curtailing the exploitation of natural resources while increasing renewable energy use and generation is one of the most important targets in sustainable development. This study investigated the impacts of women’s empowerment in politics on the green transition in developing countries. We applied a fixed-effects model to the panel data for 131 developing countries from 2000 to 2019. Our findings indicated that higher levels of participation by women in politics are likely to reduce CO2 emissions and reliance on natural resources. However, there was no statistically significant effect of women’s political empowerment on renewable energy consumption. Women’s political participation enhanced the transition towards sustainable energy sources such as geothermal and wind energy. We also found that the effects of women’s political participation on CO2 emissions were greater in countries with better political stability, government effectiveness, regulatory quality, rule of law, control of corruption, and voice and accountability. This study contributes to the growing literature in feminism and environmentalism on the achievement of sustainable development. JEL Codes: J16, O13, P48

Got It? Comparative Ergonomic Evaluation of Robotic Object Handover for Visually Impaired and Sighted Users

The rapidly growing research on the accessibility of digital technologies has focused on blind or visually impaired (BVI) users. However, the field of human–robot interaction has largely neglected the needs of BVI users despite the increasing integration of assistive robots into daily life and their potential benefits for our aging societies. One basic robotic capability is object handover. Robots assisting BVI users should be able to coordinate handovers without eye contact. This study gathered insights on the usability of human–robot handovers, including 20 BVI and 20 sighted participants. In a standardized experiment with a mixed design, a handover robot prototype equipped with a voice user interface and haptic feedback was evaluated. The robot handed over everyday objects (i) by placing them on a table and (ii) by allowing for midair grasping. The usability target was met, and all user groups reported a positive user experience. In total, 97.3% of all handovers were successful. The qualitative feedback showed an appreciation for the clear communication of the robot’s actions and the handover reliability. However, the duration of the handover was seen as a critical issue. According to all subjective criteria, the BVI participants showed higher variances compared to the sighted participants. Design recommendations for improving robotic handovers equally supporting both user groups are given.

Good Governance and Tourism in South Asian Emerging Economies: The Mediating Role of Foreign Direct Investment

This study examined the role of good governance in promoting tourism in emerging South Asian countries and also tested the mediating role of foreign direct investments (FDI). The study used panel data collected from seven South Asian countries from 1996 to 2018. Panel data regression models were estimated to test the research hypotheses. The results showed that the country’s good governance has a positive and significant influence on tourism receipts and arrivals. Moreover, the mediation analysis results supported that foreign direct investments mediated the relationship between good governance indicators like government effectiveness, political stability, voice and accountability, and control over corruption and tourism. Based on the results, the study proposed the governance-led FDI and FDI-led tourism hypotheses, the study’s main contribution. The study has implications for the government regulatory agencies relating to governance, tourism departments, foreign prospect investors, and policymakers.

Design and Development of Automated Control of Restaurant Robot

This paper presents a novel voice-controlled restaurant robot system designed to revolutionize the dining experience by seamlessly integrating advanced robotics and natural language processing technologies. The proposed system aims to enhance customer service, operational efficiency, and overall dining satisfaction by providing a user-friendly and interactive platform. .The restaurant robot is equipped with a comprehensive set of capabilities, including, voice recognition and manipulation. By leveraging voice control, users can interact with the robot using natural language commands, enabling seamless communication and intuitive control. This allows customers to effortlessly place orders The system's is comprises a mobile robotic platform, bluetooth modules, a cloud-based natural language processing, The voice control functionality is implemented through a sophisticated speech recognition system, enabling the robot to accurately understand and interpret user commands. .In addition to customer-facing interactions, the restaurant robot also assists restaurant staff in various tasks. Deliver food and beverages to tables, handle payment transactions, and perform routine cleaning tasks. By automating these processes, the system improves operational efficiency, reduces labor costs, and allows human staff to focus on highervalue activities such as customer engagement and culinary expertise. .To evaluate the performance and usability of the voice-controlled restaurant robot, a series of experiments were conducted in a real-world restaurant environment. The results demonstrated the system's high recognition accuracy, robustness in noisy environments, and its ability to effectively handle a wide range of user requests. .The proposed voice-controlled restaurant robot presents a promising solution for transforming the dining experience. It combines the convenience of voice control with advanced robotics, enabling seamless and interactive interactions between customers, staff, and the robotic platform. The system's ability to enhance customer service, operational efficiency, and overall dining satisfaction positions it as a significant advancement in the hospitality industry.

Vanderbilt Electronic Health Record Voice Assistant Supports Clinicians.

Electronic health records (EHRs) present navigation challenges due to time-consuming searches across segmented data. Voice assistants can improve clinical workflows by allowing natural language queries and contextually aware navigation of the EHR. To develop a voice-mediated EHR assistant and interview providers to inform its future refinement. The Vanderbilt EHR Voice Assistant (VEVA) was developed as a responsive web application and designed to accept voice inputs and execute the appropriate EHR commands. Fourteen providers from Vanderbilt Medical Center were recruited to participate in interactions with VEVA and to share their experience with the technology. The purpose was to evaluate VEVA's overall usability, gather qualitative feedback, and detail suggestions for enhancing its performance. VEVA's mean system usability scale score was 81 based on the 14 providers' evaluations, which was above the standard 50th percentile score of 68. For all five summaries evaluated (overview summary, A1C results, blood pressure, weight, and health maintenance), most providers offered a positive review of VEVA. Several providers suggested modifications to make the technology more useful in their practice, ranging from summarizing current medications to changing VEVA's speech rate. Eight of the providers (64%) reported they would be willing to use VEVA in its current form. Our EHR voice assistant technology was deemed usable by most providers. With further improvements, voice assistant tools such as VEVA have the potential to improve workflows and serve as a useful adjunct tool in health care.

Voice Synthesis Improvement by Machine Learning of Natural Prosody.

Since the advent of modern computing, researchers have striven to make the human-computer interface (HCI) as seamless as possible. Progress has been made on various fronts, e.g., the desktop metaphor (interface design) and natural language processing (input). One area receiving attention recently is voice activation and its corollary, computer-generated speech. Despite decades of research and development, most computer-generated voices remain easily identifiable as non-human. Prosody in speech has two primary components-intonation and rhythm-both often lacking in computer-generated voices. This research aims to enhance computer-generated text-to-speech algorithms by incorporating melodic and prosodic elements of human speech. This study explores a novel approach to add prosody by using machine learning, specifically an LSTM neural network, to add paralinguistic elements to a recorded or generated voice. The aim is to increase the realism of computer-generated text-to-speech algorithms, to enhance electronic reading applications, and improved artificial voices for those in need of artificial assistance to speak. A computer that is able to also convey meaning with a spoken audible announcement will also improve human-to-computer interactions. Applications for the use of such an algorithm may include improving high-definition audio codecs for telephony, renewing old recordings, and lowering barriers to the utilization of computing. This research deployed a prototype modular platform for digital speech improvement by analyzing and generalizing algorithms into a modular system through laboratory experiments to optimize combinations and performance in edge cases. The results were encouraging, with the LSTM-based encoder able to produce realistic speech. Further work will involve optimizing the algorithm and comparing its performance against other approaches.

Sentiment Analysis on Article Blog Post as an Application of NLP Chatbot

Within artificial intelligence, natural language processing is a relatively recent topic. With an estimated eight billion digital voice assistants in use due to their popularity, some of the most well-known instances of natural language processing in action include Apple Siri, Amazon Alexa, and, more recently, Google Duplex. With so much information gathered from these exchanges, further research and development on Natural Language Processing may be done, and it can be used in a number of sectors, such as business, technology, and healthcare. Sentiment analysis may be used, for example, in the healthcare industry to diagnose patients and build diagnostic models for early diagnosis of chronic disease. Sentiment analysis handles these large datasets more rapidly and efficiently with the use of Natural Language Industries. Chatbots, which have uses in customer service, healthcare, education, and workplace assistance, are becoming more important entry points to digital services and information. On the other hand, not much is known about how chatbots affect people individually, in groups, or in society as a whole. Moreover, a number of problems need to be resolved before chatbots can reach their full potential. As a result, in recent years, chatbots have become a prominent area of research. We propose a research agenda that outlines future objectives and challenges for chatbot research in order to further knowledge in this rapidly expanding field of study. This proposal synthesizes years of chatbot research debate at the CONVERSATIONS workshop series. utilizing a collaborative approach for study analysis among. Sentiment analysis is a technique or procedure used to identify and extract certain subjects from spoken and written language, such as beliefs and attitudes. Sentiment analysis, broadly speaking, is the capacity to evaluate the sentiment of a subject and categorize the general polarity of the topic phrase as positive, negative, or neutral (Kang & Park, 2014). Over the past ten years, sentiment analysis has gained significant scholarly attention.

How Does Interactivity Shape Users' Continuance Intention of Intelligent Voice Assistants? Evidence from SEM and fsQCA.

With the rapid expansion in the use of intelligent voice assistants (IVAs) in people's daily lives, how to improve users' continuous intention is crucial for the sustainable development of intelligent voice technology. Utilizing the stimulus-organism-response (S-O-R) framework, we propose a theoretical model to examine how three dimensions of interactivity (ie, two-way communication, responsiveness, perceived control) impact individuals' affective reactions (ie, psychological ownership, subjective well-being) and continuance intention of IVAs and how that effect differs technology readiness. To validate the proposed model, 412 valid samples were collected in China and underwent analysis using a comprehensive approach that incorporated partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The findings from the PLS-SEM analysis indicate that three dimensions of interactivity have significant impacts on affective reactions to varying degrees, thus affecting users' continuance intention. Among these dimensions, responsiveness is the strongest predictor of affective reactions. Additionally, the impact of subjective well-being on continuance intention is stronger when users with high technology readiness. Finally, the results from fsQCA support the PLS-SEM findings and provide three configurations with different combinations of antecedents that sufficiently explain high continuance intention. Our findings reveal the internal mechanisms through which the three dimensions of interactivity impact users' continued usage of IVAs. This study is among the first to examine the effects of dimensions of interactivity on behavioral intentions, utilizing both symmetric (PLS-SEM) and asymmetric (fsQCA) methodologies to identify the most significant dimensions of interactivity and determine sufficient combinations of dimensions to predict users' intention to continue using IVAs. These findings offer valuable and fresh insights for both theoretical understanding and practical application.

Real-time speech adaptations in conversations between human interlocutor and AI confederate

Compared to human-directed adaptations, less is known about how humans adjust their speech for intelligibility benefits while interacting with an AI-powered voice interface. In this study, we investigate human speech adaptations in human-to-human versus human-to-AI unscripted conversations. Specifically, we examine the production of words containing intervocalic /t-d/ in a conversation between a speaker who distinguishes these two stops (e.g., metal–medal) and a speaker (“flapper”) who merges the two stops into a flap /ɾ/. We predict that misperceptions of intervocalic /t-d/ may cause confusions, thus motivating adaptations. We record native Canadian-English speakers (flappers) while playing a video game on Zoom in two conversation settings: with (1) a human non-flapper and (2) an AI non-flapper (computer-generated speech). Acoustic analyses of the productions by human flapper speakers include features specific to stop-flap distinctions as well as global features (e.g., overall duration). In both human- and AI-directed speech, we expect human interlocutors to change flapped productions to stops to enhance intelligibility, particularly late in the conversation. Moreover, we expect differences between human- and AI-directed adaptations, with the former dominantly employing sound-specific features and the latter relying more on global hyperarticulation. Understanding these interlocutor-oriented adaptations may inform the technology behind human-computer interfaces.

Speech analysis of teaching assistant interventions in small group collaborative problem solving with undergraduate engineering students

AbstractThis descriptive study focuses on using voice activity detection (VAD) algorithms to extract student speech data in order to better understand the collaboration of small group work and the impact of teaching assistant (TA) interventions in undergraduate engineering discussion sections. Audio data were recorded from individual students wearing head‐mounted noise‐cancelling microphones. Video data of each student group were manually coded for collaborative behaviours (eg, group task relatedness, group verbal interaction and group talk content) of students and TA–student interactions. The analysis includes information about the turn taking, overall speech duration patterns and amounts of overlapping speech observed both when TAs were intervening with groups and when they were not. We found that TAs very rarely provided explicit support regarding collaboration. Key speech metrics, such as amount of turn overlap and maximum turn duration, revealed important information about the nature of student small group discussions and TA interventions. TA interactions during small group collaboration are complex and require nuanced treatments when considering the design of supportive tools. Practitioner notesWhat is already known about this topic Student turn taking can provide information about the nature of student discussions and collaboration. Real classroom audio data of small groups typically have lots of background noise and present challenges for audio analysis. TAs have little training in how to productively intervene with students about collaborative skills. What this paper adds TA interaction with groups primarily focused on task progress and understanding of concepts with negligible explicit support on building collaborative skills. TAs intervened with the groups often which gave the students little time for uptake of their suggestions or deeper discussion. Student turn overlap was higher without the presence of TAs. Maximum turn duration can be an important real‐time turn metric to identify the least verbally active student participant in a group. Implications for practice and/or policy TA training should include information about how to monitor groups for collaborative behaviours and when and how they should intervene to provide feedback and support. TA feedback systems should keep track of previous interventions by TAs (especially in contexts where there are multiple TAs facilitating) and the duration since previous intervention to ensure that TAs do not intervene with a group too frequently with little time for student uptake. Maximum turn duration could be used as a real‐time metric to identify the least verbally active student in a group so that support could be provided to them by the TAs.

Utilizing Near-Ultrasound Inaudible Trojan (NUIT) to compromise microphone security

Voice Control Systems (VCSs) provide a user-friendly interface for issuing voice commands to smart devices. However, the security of VCSs remains a challenge, as evident from two distinct attack classes: (i) inaudible attacks, feasible when the attacker is in proximity to the victim, and (ii) audible attacks, which can be executed remotely by embedding attack signals into audio. In talk introduces a novel class of attacks known as Near-Ultrasound Inaudible Trojan (NUIT). NUIT attacks combine the advantages of both classes, being both inaudible and capable of remote execution. Additionally, NUIT attacks can achieve end-to-end unnoticeability, a crucial aspect often overlooked in the literature. Notably, NUIT attacks leverage victim speakers to compromise victim microphones and associated VCSs, eliminating the need for specialized speakers. The feasibility of NUIT attacks is demonstrated, and an effective defense against them is proposed.

Blind Vision Voice Assistant

Visual Impaired Voice Assistants are software applications that have been designed to provide assistance to visually impaired people by using voice commands and text-to-speech technology. These Assistants use advanced technologies such as speech recognition, natural language processing, and text-to-speech to communicate with the user and perform tasks. The report will explore the history, technology, benefits, and limitations of Visual Impaired Voice Assistants. The history of these Assistants can be traced back to the 1960s when researchers started exploring the use of computers to help visually impaired people. In the early 2000s, Voice Assistants such as Microsoft's Cortana and Apple's Siri were introduced, which provided greater functionality and could perform a wide range of tasks using natural language processing. The technology behind Visual Impaired Voice Assistants includes speech recognition, natural language processing, and text-to-speech. Speech recognition converts spoken words into text, while natural language processing allows the Assistant to understand the context and intent of the user's input. Textto-speech technology converts digital text into spoken words. These technologies allow visually impaired users to access digital information and perform tasks without the need for visual assistance. The report will also highlight the benefits and limitations of Visual Impaired Voice Assistants. The benefits of these Assistants include increased independence, greater accessibility to digital information, and the ability to perform tasks that would have been difficult or impossible without assistance. However, the limitations of these Assistants include difficulty understanding certain accents or dialects and struggling with background noise. Finally, the report will discuss the impact of Visual Impaired Voice Assistants on the lives of visually impaired people. These Assistants have revolutionized the way visually impaired people interact with technology and the world around them. They have provided greater independence, increased accessibility, and helped to break down barriers, promoting inclusivity

Validation of system usability scale as a usability metric to evaluate voice user interfaces.

In recent years, user experience (UX) has gained importance in the field of interactive systems. To ensure its success, interactive systems must be evaluated. As most of the standardized evaluation tools are dedicated to graphical user interfaces (GUIs), the evaluation of voice-based interactive systems or voice user interfaces is still in its infancy. With the help of a well-established evaluation scale, the System Usability Scale (SUS), two prominent, widely accepted voice assistants were evaluated. The evaluation, with SUS, was conducted with 16 participants who performed a set of tasks on Amazon Alexa Echo Dot and Google Nest Mini. We compared the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. Furthermore, we derived the confidence interval for both voice assistants. To enhance understanding for usability practitioners, we analyzed the Adjective Rating Score of both interfaces to comprehend the experience of an interface's usability through words rather than numbers. Additionally, we validated the correlation between the SUS score and the Adjective Rating Score. Finally, a paired sample t-test was conducted to compare the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. This resulted in a huge difference in scores. Hence, in this study, we corroborate the utility of the SUS in voice user interfaces and conclude by encouraging researchers to use SUS as a usability metric to evaluate voice user interfaces.

Applications of Artificial Intelligence in Revolutionizing the Travel Industry

With the development of ICTs, vivid changes have been experienced in the travel industry. Artificial Intelligence (AI) is becoming an integral part of everyday lives and it is also impacting the travel industry to a great extent. The travel industry has always been among the leading industries to adopt the latest technologies. It has also acquired the AI-enabled tools for providing a seamless travelling experience to the travellers. The use of travel chatbots, customer service travel bots, voice assistants, service automation and robots have been able to fulfill the demands of modern day tourists. This eventually leads to personalization of tourism services, upgradation of operations and increment of productivity. Artificial Intelligence (AI) has proved to be of great help during the COVID-19 pandemic when there were restrictions on travelling and social distancing regulations were being followed by the travellers. It has a promising future in the travel industry as well. The present study tends to discuss the various usages of artificial intelligence in improving the travel industry. The paper also tends to highlight the impact of artificial intelligence in revolutionizing the travel industry and offering a better travel experience to the tourists.

Implementation of the Smart Car Controller System in Cars Based on the Internet of Things

<em>This research aimed to create innovations in the development of the Internet of Things in car accessories to add system accessories to cars. The type of research used was experimental research. The subjects studied in this research were car users whose hobbies were various types of cars. The use of Internet of Things media in car accessories is very unique and has not been widely used to create innovations in the development of Internet of Things technology. This research aimed to help car users control their cars through the voice control feature, as well as to make car users a second choice to upgrade their car systems compared to buying a sophisticated car at an expensive price. The research results showed that there were still very few car users equipped with control systems compared to luxury car users who had sophisticated systems. Thus, it can be concluded that the idea of a voice control system using the Internet of Things for car accessories could help car users increase the sophistication of the car system and is a good choice.</em>