Voice Recognition Technology Research Articles

Voice Search Technology and Consumer Buying Behaviour of Smart Phones in Port Harcourt

This study investigated the relationship between voice search technology and consumer buying behaviour of smartphones in Port Harcourt. Specifically, the objectives of the study were to determine how voice recognition accuracy and user experience relates to routine and impulse purchase behaviour of smartphones in Port Harcourt. The population of this study comprised of twenty-two registered mobile phone dealers in Port Harcourt. The census approach was adopted to choose the respondents for this study. To generate data for the study, researcher purposively distributed the copies of questionnaire in the frame of three (3) copies to each mobile phone firm. A total of sixty-six (66) respondents were used as the study subjects, however only fifty-eight (58) respondents provided data for the analysis. From results of the analysis it was revealed that voice recognition accuracy and user experience which are the dimensions of voice search technology positively and significantly relate with routine buying behaviour and impulse buying behaviour (measures of consumer buying behaviour). Therefore, the study concludes that the impact of voice search technology on consumer buying behavior of smartphones in Port Harcourt reveals significant correlations between the dimensions of voice search technology—voice recognition accuracy and user experience—and different buying behaviors. Furthermore, the study recommended that amongst others that Smartphone dealers should invest in advanced voice recognition technology to ensure high accuracy in voice commands. This will enhance consumer satisfaction and potentially increase both routine and impulse purchases.

Smart Dispenser Using Voice Recognition as an Assistive Device for the Visually Impaired with a Second-Order IIR Filter Algorithm

This research developed a Smart Dispenser with Voice Recognition technology as a tool to help the visually impaired in taking drinking water. The system is designed using a Voice Recognition sensor, an ISD 1820 sensor, and a microcontroller to enable the operation of the dispenser via voice commands. The test was carried out to analyze the performance of the system in recognizing voice commands and removing water. The results showed a voice detection accuracy rate of 28%, with factors such as environmental noise and sound intonation affecting the effectiveness of the system. The study also identified several areas of improvement, including the use of a DC water pump to increase the flow rate, the addition of an automatic feature to stop the flow of water when a glass is taken, and the use of a DC fan to maintain the sensor's optimal temperature. Further development is needed to improve the accuracy and efficiency of the system so that it can more effectively help the visually impaired in daily life.

Application of Deep Learning for Voice Command Classification in Turkish Language

In this study, a deep learning model was developed for the recognition and classification of voice commands using the Turkish Speech Command Dataset. The division of training, validation, and test sets was carried out on an individual basis. This approach aims to prevent the model from memorizing and to enhance its generalization capability. The model was trained using Mel-Frequency Cepstral Coefficients (MFCC) features extracted from voice files, and its classification performance was evaluated in detail. The findings indicate that the model successfully classifies voice commands with a high accuracy rate, achieving an overall accuracy of 92.3% on the test set, highlighting the potential of deep learning approaches in voice recognition technologies

Voice Recognition Based on Machine Learning Classification Algorithms: A Review

One essential component of biometric identity is voice recognition technology, which uses speech pattern analysis to authenticate people. With an emphasis on machine learning classification techniques, this review article thoroughly examines the field of speech recognition. We examine the effectiveness of random forest (RF), multilayer perceptrons (MLP), k-nearest neighbours (KNN), and support vector machine (SVM) classifiers via painstaking analysis and empirical evaluation. Utilizing a collection of Sepedi speech audio files, our results demonstrate the remarkable accuracy of 99.86% that RF is capable of producing. Aside from visual aids for better understanding, assessment indicators like as accuracy, precision, recall, F-measure, and root mean square error (RMSE) clarify the effectiveness of the model. The research highlights how machine learning algorithms, especially reinforcement learning (RF), have the capacity to revolutionize speech recognition technology in a variety of contexts.

"Alexa, Cycle The Blood Pressure": A Voice Control Interface Method for Anesthesia Monitoring.

Anesthesia monitors and devices are usually controlled with some combination of dials, keypads, a keyboard, or a touch screen. Thus, anesthesiologists can operate their monitors only when they are physically close to them, and not otherwise task-loaded with sterile procedures such as line or block placement. Voice recognition technology has become commonplace and may offer advantages in anesthesia practice such as reducing surface contamination rates and allowing anesthesiologists to effect changes in monitoring and therapy when they would otherwise presently be unable to do so. We hypothesized that this technology is practicable and that anesthesiologists would consider it useful. A novel voice-driven prototype controller was designed for the GE Solar 8000M anesthesia patient monitor. The apparatus was implemented using a Raspberry Pi 4 single-board computer, an external conference audio device, a Google Cloud Speech-to-Text platform, and a modified Solar controller to effect commands. Fifty anesthesia providers tested the prototype. Evaluations and surveys were completed in a nonclinical environment to avoid any ethical or safety concerns regarding the use of the device in direct patient care. All anesthesiologists sampled were fluent English speakers; many with inflections from their first language or national origin, reflecting diversity in the population of practicing anesthesiologists. The prototype was uniformly well-received by anesthesiologists. Ease-of-use, usefulness, and effectiveness were assessed on a Likert scale with means of 9.96, 7.22, and 8.48 of 10, respectively. No population cofactors were associated with these results. Advancing level of training (eg, nonattending versus attending) was not correlated with any preference. Accent of country or region was not correlated with any preference. Vocal pitch register did not correlate with any preference. Statistical analyses were performed with analysis of variance and the unpaired t -test. The use of voice recognition to control operating room monitors was well-received anesthesia providers. Additional commands are easily implemented on the prototype controller. No adverse relationship was found between acceptability and level of anesthesia experience, pitch of voice, or presence of accent. Voice recognition is a promising method of controlling anesthesia monitors and devices that could potentially increase usability and situational awareness in circumstances where the anesthesiologist is otherwise out-of-position or task-loaded.

AI Personal Assistant

Abstract : This paper talks about using a Voice Intelligent Assistance tool, which basically means a program that responds to your voice commands. It can help you search for things, summarize information, and set reminders, all just by talking to it. With voice recognition technology, you can access any document or file by simply saying its name. If you say a word, it will type it out for you. The tool listens to what you say, finds the right information in its database, and shows it to you. It works by first turning your spoken words into written text. You need to make sure to speak in the right language so the assistant can understand you correctly. If there’s any confusion, it will show messages on the screen to help you out. This assistant is like a software helper that does tasks based on what you tell it. It makes interacting with computer programs easier because you can just talk to them instead of typing. It can even read out summaries of reports and important information to you, making things simpler and more convenient.

Strategi Komunikasi Inovatif untuk Menjangkau Konsumen di Era Digital

In the ever-evolving digital era, communication strategies have become crucial for effectively reaching consumers. This study aims to explore and analyze innovative communication strategies that can be used to engage consumers in the digital age. The research method employed is descriptive analysis and literature review to examine various communication strategies that have been implemented in the digital context. The findings of the study reveal that innovative communication strategies such as the utilization of social media, multimedia content, and voice recognition technology have a positive impact on fostering consumer engagement and expanding market reach. The key takeaway from this research is the significance of adapting to technological trends and developing communication strategies aligned with the preferences of digital consumers to enhance marketing effectiveness and boost business competitiveness. This study contributes to a deeper understanding of how communication strategies can be tailored to the digital era to achieve better marketing outcomes

APPOINTMENT MAKER USING COMPUTERIZED VOICE

The Appointment Maker Using Computerized Voice is a novel system designed to streamline the process of scheduling appointments in healthcare settings through the integration of voice recognition and natural language processing technologies. By leveraging speech-to-text and text-to-speech capabilities, the system enables users to interact with the appointment booking interface using spoken commands, eliminating the need for manual input and enhancing accessibility for individuals with diverse technological proficiencies. This intuitive interface simplifies the appointment scheduling process, allowing users to book, reschedule, or cancel appointments with ease. Furthermore, the Appointment Maker employs machine learning models to understand and interpret user queries, providing accurate responses and assistance in real-time. By leveraging pre-trained language models and fine-tuning them on domain-specific data, the system demonstrates robust performance in understanding diverse user inputs and generating contextually relevant responses. This capability enhances user experience by providing personalized and timely assistance, akin to interacting with a human receptionist.The Appointment Maker features a backend database for storing and managing appointment and user information, ensuring data integrity, security, and efficient resource utilization. The relational database schema facilitates seamless coordination between users, healthcare providers, and administrative staff, enabling efficient scheduling and management of appointments. Overall, the Appointment Maker Using Computerized Voice represents a significant advancement in healthcare administration, offering a user-friendly and efficient solution for appointment scheduling while improving accessibility and patient experience. Key Words: Computerized Voice ,Natural Language Understanding (NLU), Speech to Text, Text to Speech.

Artificial Intelligence: A Quick Overview of Speech Recognition System

Abstract: The study of intelligent behaviour is known as artificial intelligence. Wagner said, "We build intelligent machines in science." By this, he implied that the primary goal is to teach these sophisticated devices, such as computers, to examine and comprehend human conduct. numerous believed that human intelligence might be understood through the construction of several programmes, while other researchers believed that numerous basics should be required in order to accomplish the intended objective. We can carry out or replicate any type of task we conduct using a computer. Therefore, computers are the ideal devices for developing artificial intelligence. In the hopes that they will outperform computers in several other areas, some researchers created additional computing devices. Billions of dollars were invested in creating new devices that surpassed the speed of the computer used to simulate software. However, the computers and the code both need to be extremely quick. As you learn about artificial intelligence, you may wonder, what really is intelligence? How is intelligence measured? Turn become something quite significant. In this case, the programme that evaluates its surroundings acts as the intelligent agent. Modern digital computers and human minds are comparable to one other in ways such as symbolic information processing systems. Both use symbolic data as input, change it in accordance with predetermined guidelines, and then use the results to solve issues. In order to mechanically replicate intelligent human activity, artificial intelligence researchers have programmed some algorithms to recognise intelligent human behaviour. Human conduct in language processing, chess, and other games, as well as medical diagnostics, may be used to monitor this activity. Voice recognition is a key component of AI. A certain voice may be recognised using voice recognition technology. The cornerstone for speaker identification is voice signals. Voice targeting is applicable to a wide range of applications, including voice mail, database access, phone banking, and phone purchasing. The ability to enter one's voice for verification is among the most potent uses of voice recognition for security. The fundamental means of interpersonal communication is speech. The technique of translating voice sounds into appropriate text is known as speech recognition. Over the past few years, speech recognition technology has advanced significantly. Nonetheless, there are other significant study obstacles, such as variations in speaker and language, ambient sound, word size, etc. This study aims to give a comprehensive overview of speech acceptance by summarising the several approaches utilised in the standard speech system and describing the numerous processes involved

V App: E College System for Blind Students

n India, the visually impaired community constitutes a substantial portion, accounting for nearly 60% of the global blind population. As society evolves, addressing the needs of marginalized groups, particularly those with disabilities, becomes increasingly imperative. A critical requirement is ensuring improved mobility for the blind, an ongoing challenge despite numerous initiatives. With the rapid advancements in wireless communication, the demand for effective voice recognition technologies has surged. Voice-based applications, employing interfaces and dialogue management, offer a promising solution, enabling users to focus on tasks without manual engagement and enhancing independence in navigation and educational access. Keywords: Accessibility, E-college system technology, Visually impaired, Voice recognition.

Desktop Assistant using Machine Learning

The post-2017 era witnessed a surge in desktop assistants and voice searches, driven by advancements in technology, notably in mobile phones and desktops. Breakthroughs in AI, NLP, and ML empowered devices to understand and respond to human commands effectively, laying the groundwork for voice-driven functionalities. Mobile phones and desktops evolved as central hubs for digital activities, necessitating seamless integration and user-friendly interfaces. Desktop assistants serve as bridges, facilitating intuitive interaction. The demand for faster and more reliable input led to the development of desktop assistants leveraging voice recognition technology, offering hands-free interaction. Desktop assistants make technology interaction convenient and enjoyable by incorporating natural language understanding, allowing fluid conversations and enhancing user experience. AI plays a crucial role, enabling assistants to comprehend context, learn, and adapt to preferences, providing personalized and contextually relevant services. Their applications span personal productivity and smart home automation, offering assistance in tasks like setting reminders and controlling connected devices. Despite advancements, challenges like privacy concerns and data security require attention. Balancing convenience and privacy remains a paramount challenge. Future trends include enhancements in NLP, increased personalization, and integration with emerging technologies like AR and VR, promising more immersive interactions. Keywords: Voice searches, Advancements in technology, AI, ML, User-friendly interfaces, Voice recognition technology, Natural language understanding, Personalized services

Jarvis-Virtual Voice Assistant

Although voice recognition technology has advanced significantly, there are still obstacles in the way of obtaining high accuracy, especially in a variety of environmental settings. This research uses deep learning models to increase the robustness and accuracy of voice recognition systems. Implementing different deep learning architectures, such as recurrent and convolutional neural networks (RNNs), and training them on a wide range of datasets with different noise levels and accents was part of the technique. Important results show a 15% improvement in recognition accuracy over current systems for the suggested models, which perform especially well in noisy contexts and with accented speech. These outcomes demonstrate the effectiveness of deep learning techniques in resolving issues that traditional voice recognition systems encounter. The results of this study have important significance for real-world applications, since they may enable smooth communication in a variety of linguistic and environmental contexts. Context: Since voice recognition technology has come a long way, it still has problems with accuracy, particularly in noisy situations and different locations with different accents. Even with advancements, traditional systems find it difficult to function consistently and dependably under these circumstances. This lays the groundwork for the need to investigate more durable ways to raise voice recognition's accuracy and dependability.

TalkTrek: Journeying Through the Terrain of Text-based Speech Synthesis

Abstract: This research paper delves into the development and evaluation of TextTalker, an innovative voice-enabled education chatbot designed to enhance learning experiences. TextTalker integrates advanced natural language processing capabilities with voice recognition technology to facilitate seamless interactions between users and educational content. Furthermore, it incorporates a unique feature allowing users to access relevant YouTube videos directly within the chat interface, fostering a multi-modal learning environment. The system's architecture and design are meticulously crafted to ensure user engagement, accessibility, and effectiveness in delivering educational materials. Through a series of usability studies and user feedback analysis, this research evaluates the efficacy of TextTalker in supporting learning objectives and enhancing user satisfaction. The findings reveal promising results, indicating high levels of user engagement and satisfaction with the platform's intuitive interface and diverse learning resources. Moreover, incorporating feedback mechanisms empowers users to contribute to the refinement and enhancement of TextTalker's functionalities, fostering a collaborative learning ecosystem. Overall, TextTalker represents a significant advancement in educational technology, offering a user-centric approach to interactive learning through voice-enabled chatbots integrated with multimedia content delivery and feedback mechanisms

Smart Electronic Noticing System Through Voice Alert and Text Message

Abstract: In the era of smart technologies, this project introduces an approach to streamline and enhance the process of electronic noticing by incorporating the voice commands and text messaging capabilities. The Smart Electronic Noticing System via voice and text message features reduces the reliance on traditional noticing system. This system is developed to offer an effective and user-friendly platform for managing announcements in various places such as shopping malls, traffic signals, hospitals, railway stations, bus stations, offices, and other public spaces. The system uses advanced voice recognition technology through android application to allow user to interact with the noticing system through spoken commands. Additionally, users can conveniently send notices via text messages with the help of telegram bot, further expanding the system's versatility. By the combination of voice command and text message features, the Smart Electronic Noticing System aims to redefine the way of user interact with and manage notices. Apart from the voice and text message capabilities, the system also has a capability to display the current atmospheric temperature with some pre- defined delay. This project aligns with the growing demand for accessible, and efficient smart solutions in today's technologically advanced world.

시각장애인의 TV홈쇼핑 사용 환경에 관한 연구

Due to the importance of corporate social responsibility and sustainable management, ESG (Environmental, Social, and Governance) management is becoming an essential condition for modern companies. In order to find out how the TV home shopping industry is responding to this social consensus, this study identified the current situation of the TV home shopping experience of the visually impaired. As a research method, this study examined the problems pointed out in previous studies and related media articles on the use of home shopping by the visually impaired, and conducted interviews with officials from seven domestic TV home shopping companies and 10 data home shopping companies to find out how the industry is responding to these problems. Through these interviews, we identified what improvements are needed for TV home shopping platforms that rely on visual elements as the main way to deliver information. Specifically, we found that the use of voice recognition technology, screen reader compatibility, in-broadcast service voice descriptions, expansion of telephone consultation services, and provision of information through alternative text and screen readers should be strengthened. In addition, physical factors such as customized TVs and Relumino assistive devices should be introduced, and real-time communication platforms such as live commerce should be expanded to enhance user engagement. The findings of this study can be utilized as ideas for rethinking the accessibility of TV home shopping experiences for the visually impaired as well as digital services in general.

An Efficient Object Detection System for the Blind People

As object recognition technology has developed recently, various technologies have been applied to autonomous vehicles, robots, and industrial facilities. However, the benefits of these technologies are not reaching the visually impaired, who need it the most. In this research, researchers proposed a deep learning based on object identification system for the visually impaired. Voice recognition technology is used to know what objects a blind person wants, and then to find the objects via object recognition. Furthermore, a voice guidance technique is used to inform sight impaired persons as to the location of objects. The object recognition deep learning model utilizes the single shot multi-box detector (SSD) neural network architecture, and voice recognition is designed through speech-to-text (STT) technology. In addition, a voice announcement is synthesized using text-to-speech (TTS) to make it easier for the blind to get information about objects. The system is built using python OpenCV tool. As a result, we implement an efficient object-detection system that helps the blind find objects in a specific space without help from others, and the system is analyse through experiments to verify performance. Keywords: Object detection system, blind people, Open CV, Voice Recognition

DEEP LEARNING IN SPEAKER IDENTIFICATION: MODERN METHODS AND DEVELOPMENT PROSPECTS

The article contains a review of the research on the use of deep learning in speaker identification. It examines the problems of voice identification, highlighting the relevance and the need for effective methods in this area. The evolution of speaker identification techniques from simple pattern matching to complex neural architectures is traced to understand the technological advancements in this field. Modern methods for speaker identification and the prospects for the development of such systems are considered. The two aims set by the authors are: to make comparative analysis of deep learning with traditional methods and to review the current state of technology. First they highlight the key differences and advantages of deep learning compared to traditional approaches to speaker identification, describe the challenges in deep learning methods, such as the necessity for large datasets and computational resources, and analyse how these issues are addressed by the research community. Then the authors provide a comprehensive overview of the current deep learning methods used for speaker identification, including the latest breakthroughs and innovations in neural network architectures, training techniques, and feature extraction methods. The potential of unsupervised and semi-supervised learning paradigms to further enhance speaker identification systems is explored, offering insights into the future research in this field. Key words: deep learning, speaker identification, neural networks, recurrent layers, convolutional layers, voice recognition technologies.

Application of Voice Recognition Technology for Early Detection of Respiratory Disorders in Premature Babies

Application of Voice Recognition Technology for Early Detection of Respiratory Disorders in Premature Babies

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.

Design and implementation of domestic dual-SIM telesecurity alarm system using voice code recognition

Violent crime cases which include, robbery, rape, and homicide are terribly on the rise, and the role of security in combating this menace cannot be overemphasized. This research presents a security device that aims at fighting violent crimes using voice recognition technology. The work also tends to solve the issue of network downtime when the user is out of reach for help in time of attack. In this work, a voice processing unit which comprises the condenser microphone, an amplifier, a shift register, and a timer was designed. The processing unit circuit was incorporated into microcontrollers which create Human-Device interaction and the GSM communication unit which is made up of two GSM modules. The two microcontrollers used in the design are PIC18F4520 and PIC16F873A. The microcontrollers were programmed with C++ using the MPLAB IDE software and the circuit simulation was done using Proteus Design Suite version 8. The result shows that the appropriate authority receives SMS whenever the pre-recorded code is mentioned. The result also shows that during network downtime, the second GSM module sends an SMS to the appropriate authority. Evaluating the performance of this work, it was observed that the device works best in a calm area compared to a noisy area. This work is designed to work in domestic areas like homes, offices, malls, and mainly areas free from so much noise. Therefore, this work has successfully reduced the crime rate in emergencies.