Voice Recognition Research Articles

EmoSonics: Emotion Detection via Voice and Speech Recognition

Understanding emotions from speech is like deciphering a rich tapestry of human expression in the realm of human-computer interaction. It's akin to listening to someone's tone and inflection to discern whether they're happy, surprised, or experiencing a range of other feelings. Researchers use a variety of techniques, from analyzing speech patterns to utilizing advanced technologies like fMRI, to decode these emotional cues. Emotions aren't just simple labels; they're complex and nuanced, demanding sophisticated methods for accurate interpretation. Some methods break emotions down into simple categories, while others embrace the intricacies of human emotion, treating them as continuous variables. Ultimately, the goal is to enhance communication between humans and computers by enabling machines to understand and respond appropriately to our emotional states. This pursuit underscores the significance of emotion detection in speech analysis and highlights the need for continually evolving methodologies in human-computer interaction research.

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.

A New Wave in Biometric System: Systematic Study incorporated with Artificial Intelligence

Biometric system is a technique used to identify a person using its personal identification methods. The main concept of biometric systems is to provide confidentiality and security to the user. A number of biometric systems are introduced but some systems are widely used and are famous because of their usage and security they provide. Physiological and Behavioral biometrics are the two types of biometric systems. Biometric systems include physiological biometric like face recognition, finger print recognition, ir is recognition and behavioral biometrics like signature recognition and voice recognition. All these recognition systems are discussed in this research paper. Biometric systems work on three levels: Enrollment, Verification, and Identification. Enrollment is the process in which patterns are captured from the user and stored in the database. Verification means to confirm that the sample entered by the user belongs to him or not. When the user wants to access the data then the user must use his/her biometrics that the systematic hacks that the person who wants to access the data is the real owner of the data or not. This process is identification. All three levels are the working levels of the Biometric System. In earlier years, biometrics was used only at ground levels to provide basic security to data but now the tables have turned. It is playing a major role in providing security to our data. Biometrics are not only used in day-to-day life in phone unlocking, phone assistants, attendance systems but also used at advanced levels like in airports, border security, cloud computing etc. In this research paper, we will discuss the future scope of biometric systems and how it could even change the future.

Flexible Self-Powered Low-Decibel Voice Recognition Mask.

In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts.

Enhanced Accessibility for Visually Impaired & Blind Artists (EAVIBA) using Machine Learning

This research paper introduces EAVIBA, an innovative voice-driven interface aimed at empowering individuals with visual impairments and blindness in the domain of art and creativity. Rooted in principles of accessible design, EAVIBA integrates advanced features including voice commands, text-to-speech functionality, and a customizable graphical user interface, exemplifying a commitment to inclusive technology. The paper delves into the intricacies of EAVIBA's development, elucidating its architectural framework, seamless integration of assistive technologies, and adherence to recognized accessibility standards such as the Web Content Accessibility Guidelines (WCAG). Employing a comprehensive approach, the research paper outlines the iterative process of user testing, providing both qualitative and quantitative insights into critical aspects such as interface clarity, voice command recognition accuracy, and the efficacy of customization options. The results of these evaluations affirm EAVIBA's remarkable success in delivering an inclusive, accessible, and empowering environment for individuals with visual impairments to engage in artistic expression, thereby contributing significantly to the broader landscape of assistive technologies and accessible interfaces. Keywords: Visually impaired, blind artists, voice-driven interface, assistive technologies, voice recognition, machine learning.

NIKKY THE HUMANOID ROBOT FOR HEALTHY AND SAFETY LIVING

As the global population ages, the demand for effective elderly care solutions has become increasingly urgent. With advancements in technology, there arises an opportunity to revolutionize the way we approach elderly care, moving beyond traditional models towards more innovative and integrated approaches. This project introduces an integrated smart home automation system designed to enhance the well-being and independence of seniors while providing invaluable support to caregivers. Leveraging cutting-edge technologies such as AI integration, IoT devices, and voice recognition, the system offers a seamless and intuitive user experience. Features include automated task management, real-time environmental monitoring, medication management, and emergency response capabilities. By streamlining daily routines, promoting social engagement, and ensuring safety, the system aims to improve the overall quality of life for seniors while alleviating stress and burden for caregivers. Additionally, this project explores the potential of humanoid robot technology, exemplified by robot Nikky, to further augment elderly care through human-like interactions and assistance. This integrated approach not only addresses physical needs but also focuses on cognitive stimulation and emotional well-being, fostering a sense of companionship and purpose for seniors. Through these efforts, this project seeks to address the evolving needs of aging populations and contribute to the advancement of technology-driven solutions in elderly care and support services. Key Words: Home automation, Robotics, Smart and healthy living, Humanoid Robot for personal interaction

VOICE CONTROLLED WHEELCHAIR FOR PHYSICALLY DISABLED PEOPLE

As the time being, Technology is growing at a faster rate. Improvement in Technology makes the lives simple. everyone wants to live individually using the technology. However, this is deceptive for physically challenged persons. Majority of people with disabilities having trouble moving various body parts. The only solution is to depend on their family and friends to complete their day to day works. Even for moving from one place to other place the persons must use a wheelchair. For moving the wheelchair, the person should have enough strength in hands or requires other person to assist the wheelchair. The persons having disability are less communicating with the outside world which make them week and reluctant. The proposed machine is a wheelchair- manipulated gadget to help disabled human beings to turn out to be unbiased. The device uses a voice recognition unit which will take human voice as command and control the wheelchair movement based on the command. The device integrates Arduino, HC-05 Bluetooth module, speech-to-text voice recognition android utility, and motor driver through which the customers can perform the wheelchair with the aid of absolutely speaking to the wheelchair microphone. The wheelchair is designed at low cost in such a way that needy users will benefit from the machine. Key Words- voice recognition, HC-05 Bluetooth module, physically challenged persons.

Design of interactive online teaching system for big data computer information system based on mobile sensor network

The combination of the Internet and computers has resulted in many new products, including smart homes. The emergence of smart homes can satisfy people's detection of the internal environment of the home, not only to ensure the continuity and safety of indoor monitoring, but also to provide convenience for people to manage their family life. This paper analyzes the current status of smart home research at home and abroad, and people's needs for homes, and builds and designs smart home systems based on Ethernet and ZigBee protocol wireless sensor networks connected to TCP/IP protocol. In this article, the ARM chip is used in the Android system to ensure that the system and functions are matched, and information is transmitted through the serial port of the development board. After receiving the corresponding sending signal, the wireless module will use ZigBee technology to send the corresponding signal to achieve the goal of controlling home appliances. We divides the control system into two parts, the Android Voice Control Platform and the data collection terminal. The former is based on the Android operating system and is designed for voice recognition control; the latter uses a ZigBee microcontroller to achieve control and feedback. This article uses the built-in voice control system of the smart home to subtly replace the conventional control method of the smart home. Moreover, the dialogue with users is very high, easy to use, low cost, and very practical. In addition, the research methods used in this article can also be applied to other related fields.

The Current State and Future Trends of AI-assisted Higher Vocational English Education

As information technology evolves, particularly with advancements in artificial intelligence (AI), higher vocational English education is witnessing significant transformations. This paper explores the integration of AI in teaching methods and predicts future trends. AI applications, such as automated assessments and personalized learning paths, enhance teaching efficiency and personalize education. Technologies like natural language processing (NLP) evaluate students' language skills in real-time, providing instant, objective feedback which lightens educators' workloads and increases assessment precision. AI-driven tools, including language learning software like Duolingo and virtual teaching assistants, utilize voice recognition and machine learning to simulate interactive learning environments. These technologies adjust teaching content dynamically and help students practice English in realistic scenarios, thereby improving language proficiency and motivation. Future advancements in AI are expected to introduce more sophisticated algorithms for complex tasks like discourse analysis and cultural education, supported by evolving big data technologies. This will enable deeper, more precise learning analytics and content customization, further enhancing educational outcomes. The integration of AI not only enriches resources but also fosters educational model innovation, supporting vocational colleges in training globally competent professionals. As AI continues to develop, its role in shaping higher vocational English education will grow increasingly vital.

Neural basis of language familiarity effects on voice recognition: An fNIRS study

Recognizing talkers' identity via speech is an important social skill in interpersonal interaction. Behavioral evidence has shown that listeners can identify better the voices of their native language than those of a non-native language, which is known as the language familiarity effect (LFE). However, its underlying neural mechanisms remain unclear. This study therefore investigated how the LFE occurs at the neural level by employing functional near-infrared spectroscopy (fNIRS). Late unbalanced bilinguals were first asked to learn to associate strangers' voices with their identities and then tested for recognizing the talkers' identities based on their voices speaking a language either highly familiar (i.e., native language Chinese), or moderately familiar (i.e., second language English), or completely unfamiliar (i.e., Ewe) to participants. Participants identified talkers the most accurately in Chinese and the least accurately in Ewe. Talker identification was quicker in Chinese than in English and Ewe but reaction time did not differ between the two non-native languages. At the neural level, recognizing voices speaking Chinese relative to English/Ewe produced less activity in the inferior frontal gyrus, precentral/postcentral gyrus, supramarginal gyrus, and superior temporal sulcus/gyrus while no difference was found between English and Ewe, indicating facilitation of voice identification by the automatic phonological encoding in the native language. These findings shed new light on the interrelations between language ability and voice recognition, revealing that the brain activation pattern of the LFE depends on the automaticity of language processing.

The politics of voicelessness: voices of community elders in ethic and religious minorities in the Philippines

The study aims to examine the phenomena of minoritization, which involves the imposition of the beliefs and culture of dominant groups onto minority groups, leading to social exclusion and conflicts. The study utilizes data analysis to identify themes related to the process of minoritization. The study illustrates that minoritization occurs when dominant groups perceive themselves as superior and endeavor to dominate others, resulting in violence, oppression, and social exclusion. This study focuses on the perspectives of grassroots voices, going beyond traditional inquiries on equalities between different groups to provide a deeper insight into the systemic challenges encountered by minority communities. It emphasizes attributes such as dominant groups imposing their beliefs on others, perceiving them as inferior, and forcing them to adopt their culture or traditions. To address the issue of minoritization, the study emphasizes the importance of carrying out dialogues at all levels, promoting understanding across various cultures and religions, implementing policies that protect the rights of minority groups, and preventing systematic minoritization. This study enhances broader discussions on inclusivity and equality through exploring its findings in relation to global issues of prejudice. It advocates for the increased recognition and valued of minority voices in society. This emphasizes the need for holistic strategies that promote mutual respect among different communities and prevent conflicts arising from social exclusion.

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.

A novel hybrid database security management technique

Since the advent of the internet and the digitization of information, more individuals and organizations have gained access to the internet, leading to a significant expansion in the size of our database systems. These databases are relied upon for day-to-day activities by individuals and organizations. Furthermore, the advancements in technology have increased the vulnerability of database management systems and enforcing adequate database security measures is of paramount importance to individuals and organizations. In this study, we deployed a hybrid approach to database security system that integrates two techniques: dynamic time-warping algorithm and voice recognition methods. The implementation follows Object-Oriented Analysis and Design procedures, utilizing SQL Management Studio 2014 for the backend and ASP.NET C# for the frontend. Our hybrid approach demonstrates enhanced database security compared to previously employed authentication measures.

The Design of a Speech Recognition-Based Finger Training Rehabilitation Device: Mechanical and System Aspects

Aiming to address the extended diagnosis and recovery period along with low efficiency in traditional stroke patient rehabilitation, this study introduces a speech recognition-based finger rehabilitation training control system. This system enables patients to perform finger exercises while providing feedback on finger angle, speed, and position. Furthermore, it offers rehabilitation physicians valuable data for evaluation and reference in finger rehabilitation. The system is divided into hardware circuitry, a lower computer control system, and a voice recognition human-computer interaction system, all working in conjunction with the finger movement perception system. By applying the Hidden Markov Model (HMM) algorithm to the voice interaction system for pattern matching, the finger rehabilitation system undergoes simulation testing. The results demonstrate that the proposed rehabilitation training system based on speech recognition meets design requirements, ensuring safety, reliability, and substantial application potential in future finger rehabilitation training.

VOICE AND JOYSTICK-CONTROLLED MULTIFUNCTIONAL WHEELCHAIR

ur project is designed to address the needs of individuals facing physical disabilities or advanced age, resulting in limited mobility. The core functionality of our project relies on a voice recognition module and joystick controller, meticulously integrated with electric motors, which effectively interpret user-spoken commands to navigate the wheelchair. Furthermore, we will incorporate a sophisticated Android application, compatible with smartphones, to seamlessly interface with the various components of the wheelchair, enhancing its overall control and functionality. Central to our project's operation is an Arduino microcontroller circuit, skillfully interfaced with high-performance DC motors to facilitate precise and reliable movement. Keywords: Arduino, Voice recognition module, Joystick Controller, Smartphones, Voice Application

A dual-symmetry triboelectric acoustic sensor with ultrahigh sensitivity and working bandwidth

Triboelectricity in materials has emerged as an innovative and increasingly researched approach for acoustic sensing. Despite its promise, the field has faced significant challenges due to limited working bandwidth and sensitivity. As a result, we have developed a dual-symmetry triboelectric acoustic sensor (DS-TAS), featuring an expansive working bandwidth from 20 Hz to 200 kHz, exceptional sensitivity of 6.67 V/Pa, and a detection limit as low as 0.002 Pa. Moreover, the DS-TAS has been successfully integrated into a real-time, multi-directional ultrasonic radar system, achieving an impressively minimal detection error of less than 5 mm. This breakthrough in acoustic sensing performance positions the DS-TAS as a significant milestone for the field, with broad applications ranging from human-machine interaction to voice recognition.

Multimodal Biometric Authentication System Using of Autoencoders and Siamese Networks for Enhanced Security

Ensuring secure and reliable identity verification is crucial, and biometric authentication plays a significant role in achieving this. However, relying on a single biometric trait, unimodal authentication, may have accuracy and attack vulnerability limitations. On the other hand, multimodal authentication, which combines multiple biometric traits, can enhance accuracy and security by leveraging their complementary strengths. In the literature, different biometric modalities, such as face, voice, fingerprint, and iris, have been studied and used extensively for user authentication. Our research introduces a highly effective multimodal biometric authentication system with a deep learning approach. Our study focuses on two of the most user-friendly safety mechanisms: face and voice recognition. We employ a convolutional autoencoder for face images and an LSTM autoencoder for voice data to extract features. These features are then combined through concatenation to form a joint feature representation. A Siamese network carries out the final step of user identification. We evaluated our model’s efficiency using the OMG-Emotion and RAVDESS datasets. We achieved an accuracy of 89.79% and 95% on RAVDESS and OMG-Emotion datasets, respectively. These results are obtained using a combination of face and voice modality.

Development of an Automatic Baby Cradle System

Infant care can be challenging in the fast-paced atmosphere of today, where parental responsibilities can compete with rigorous employment. An important step toward improving newborn health and reducing parent concern is the incorporation of an autonomous baby cradle system. This cutting-edge framework combines thorough planning with the seamless incorporation of several childcare components, providing carers with a priceless tool. Modern technology is incorporated into the system, including an Espressif Systems32 camera that allows parents to monitor children from a distance, a voice recognition system that enables swinging to react to spoken cues, and an advanced moisture sensor that monitors changes in bedding conditions. A sensor boosts the system’s effectiveness and adds to its excellent mobility and versatility. In a hectic setting, the Automatic Baby Cradle System redefines infant care. This system offers a comprehensive solution that addresses several aspects of newborn well-being by effortlessly integrating technology into caring. Modern capabilities like speech recognition, temperature management, wetness sensing, and remote monitoring show how technology has the power to fundamentally alter parenting habits. This research lays the framework for future developments and advances in newborn care and parental assistance as technology advances