Voice Recognition Processing Research Articles

The Chinese Painting-style Animation Innovation in the AI Era

In the era of artificial intelligence, Chinese painting-style animation is undergoing an unprecedented transformation. The application of AI technology not only improves the efficiency of animation production but also brings new possibilities for innovation to creators. The impact of the AI era on the animation industry is multi-dimensional. AI technology can automate many repetitive tasks, such as the generation of in-between frames, the drawing of characters and scenes, and its application in various other aspects, thereby shortening the production cycle, reducing actual time costs, and improving timeliness. Its detailed functions, such as AI-assisted scriptwriting and directing to generate creative storylines, plots, and even scripts, provide new ideas for animation creation. One of the most admired and sought-after aspects is the operability and personalization of AI. By analyzing audience preferences and behaviors through various cases and traffic analysis, it can provide personalized content recommendations using animation production techniques, meeting the psychological needs of different audience groups. In terms of image and video processing, it can be used to enhance the visual effects of animation, such as automatic coloring, background rendering, image clarity and blurring processing, etc. For the design of characters and scenes, designers can quickly generate concept drawings of characters and scenes through subjective expression, including composition positioning, character body dynamics, and simulating different styles and schools of painting techniques. In terms of sound, it is also excellent, capable of simulating specific sound effects, generating and improving dubbing sound effects and sound quality, enhancing the auditory experience of animation, and through analysis techniques such as voice recognition and natural language processing, allowing animated characters to use lifelike body movements and facial expressions to interact with the audience, providing a better interactive experience.

Artificial Intelligence as a Tool for Creating Patient Visit Summary: A Scoping Review and Guide to Implementation in an Erectile Dysfunction Clinic.

In modern healthcare, the integration of artificial intelligence (AI) has revolutionized clinical practices, particularly in data management and patient visit summary creation. Manual creation of patient summary is repetitive, time-consuming, prone to errors, and increases clinicians' workload. AI, through voice recognition and Natural Language Processing (NLP), can automate this task more accurately and efficiently. Erectile dysfunction (ED) clinics, which deal with specific pattern of conditions together with an involvement of broader systemic issues, can greatly benefit from AI-driven patient summary. This scoping review examined the evidence on AI-generated patient summary and evaluated their implementation in ED clinics. A total of 381 articles were initially identified, 11 studies were included for the analysis. These studies showcased various methodologies, such as AI-assisted clinical notes and NLP algorithms. Most studies have demonstrated the ability of AI to be used in real life clinical scenarios. Major electronic health record platforms are also integrating AI to their system. However, to date, no studies have specifically addressed AI for patient summary creation in ED clinics.

RISH Assistance: Voice Activated Digital Assistant for Developers

The increasing use of computing devices brings convenience but also challenges. Traditional interfaces can lead to repetitive strain, slow interactions, and accessibility problemsfor software developers, who often find it hard to turn complex ideas into code, especially with unfamiliar APIs. In response, there is a growing interest in automation and smart machines that act like humans. However, a major obstacle remains—the gap in human-machine interaction. Introducing “RISH Assistance,” a voice-activated digital assis- tant designed for developers. RISH enables users to navigate tasks more efficiently, reducing reliance on screens and tra- ditional input methods. Utilizing advanced voice recognition and Natural Language Processing (NLP), RISH transforms the developer experience by providing easy access to resources, seamless code suggestions, and natural troubleshooting—all through voice commands. Additionally, it can manage phone functions, allowing calls and messages directly from the desktop interface, enhancing productivity. Security is ensured with face authentication, and current session chat history tracking allowsusers to revisit previous interactions. RISH boosts productivity through hands-free interaction, en- abling developers to focus on creative problem-solving. It im- proves accessibility by offering an alternative input method and streamlines workflows, reducing cognitive load while fostering a more intuitive development environment. By facilitating natural and efficient engagement with machines, RISH Assistance aims to enhance human-machine collaboration and create a more inclusive software development landscape.

Generation of black-box adversarial attacks using many independent objective-based algorithm for testing the robustness of deep neural networks

Deep neural networks (DNNs) have become increasingly ubiquitous in our daily lives, finding applications in areas such as image recognition, voice recognition, and natural language processing. However, a growing concern revolves around the vulnerability of DNNs to adversarial examples—malicious inputs that can compromise the safety and accuracy of their outcomes. Existing studies predominantly fall into two categories: white-box and black-box techniques. White-box techniques require detailed internal information about the model, often focusing on gradient-based methods. In contrast, black-box techniques, which emulate real-world scenarios more closely, only rely on input–output knowledge. This study focuses specifically on black-box strategies, with key contributions from Single-Objective Variant of Differential Evolution (Pixel-SOO) and Multi-Objective Variant of Differential Evolution (Pixel-MOO) algorithms. While these approaches show promise, they suffer from drawbacks like long execution times and being unable to generate instances for adversarial purposes. To address these challenges, we introduce a novel archive-based Many Independent Objective (MIO) algorithm for using the first time in this context. Our proposed algorithm identifies the most vulnerable image pixels through the MIO algorithm, enabling efficient label flip attacks with minimized attempts. Furthermore, we balance exploration and exploitation by incorporating an adaptive parameter mechanism. The effectiveness of the proposed algorithm is assessed by employing VGG (VGG16 and VGG19) and ResNet (ResNet50, ResNet101, and ResNet152) architectures, both of which are convolutional neural network (CNN) models. The success criterion for the algorithms is to minimize the number of pixel changes while achieving high flip rates with a minimal number of requests to the network. A comprehensive analysis of the experimental results reveals that our algorithm consistently outperforms Pixel-SOO and Pixel-MOO, exhibiting an average speedup of three times compared to Pixel-SOO and seven times compared to Pixel-MOO. In most runs, adversarial attacks are generated with fewer pixel changes than Pixel-MOO and Pixel-SOO. In addition, our findings are openly accessible on GitHub to ensure transparency and reproducibility and encourage future research efforts.

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 and Expert Systems

Artificial Intelligence (AI) is a specialized field of computer science focused on creating machines that can emulate human intelligence and behaviour. The term "Artificial Intelligence" was first introduced by John McCarthy in 1956 at the Massachusetts Institute of Technology (MIT) in the USA. AI encompasses a variety of applications including game playing, expert systems, natural language processing, neural networks, and robotics. As of now, no computer systems have achieved true artificial general intelligence, which would enable them to perform any intellectual task that a human can. However, significant progress has been made in specific domains, particularly in game playing. Modern computer chess programs, for instance, have surpassed human players in terms of skill and performance. In the early 1980s, expert systems were heralded as the future of AI and computing. These systems are designed to mimic the decision-making abilities of human experts in specialized fields such as medicine and engineering. Despite their potential, expert systems have not fully met the high expectations set for them. They tend to be costly to develop and maintain, and their utility is often limited to specific, well-defined tasks. Currently, neural networks represent one of the most dynamic and rapidly advancing areas of AI. These algorithms, inspired by the structure of the human brain, have demonstrated success in various applications, including voice recognition and natural language processing. When it comes to programming AI applications, LISP (List Processing) and Prelog (Programming in Logic) are two of the most widely used languages due to their suitability and flexibility for AI-related tasks.

An Integrated Voice Recognition and Natural Language Processing Platform to Automatically Extract Thoracolumbar Injury Classification Score Features From Radiology Reports

Many predictive models for estimating clinical outcomes after spine surgery have been reported in the literature. However, implementation of predictive scores in practice is limited by the time-intensive nature of manually abstracting relevant predictors. In this study, we designed natural language processing (NLP) algorithms to automate data abstraction for the thoracolumbar injury classification score (TLICS). We retrieved the radiology reports of all Mayo Clinic patients with an International Classification of Diseases, 9th or 10th revision, code corresponding to a fracture of the thoracolumbar spine between January 2005 and October 2020. Annotated data were used to train an N-gram NLP model using machine learning methods, including random forest, stepwise linear discriminant analysis, k-nearest neighbors, and penalized logistic regression models. A total of 1085 spine radiology reports were included in our analysis. Our dataset included 483 compression, 401 burst, 103 translational/rotational, and 98 distraction fractures. A total of 103 reports had documented an injury of the posterior ligamentous complex. The overall accuracy of the random forest model for fracture morphology feature detection was 76.96% versus 65.90% in the stepwise linear discriminant analysis, 50.69% in the k-nearest neighbors, and 62.67% in the penalized logistic regression. The overall accuracy to detect posterior ligamentous complex integrity was highest in the random forest model at 83.41%. Our random forest model was implemented in the backend of a web application in which users can dictate reports and have TLICS features automatically extracted. We have developed a machine learning NLP model for extracting TLICS features from radiology reports, which we deployed in a web application that can be integrated into clinical practice.

Integration of an RSA-2048-bit public key cryptography solution in the development of secure voice recognition processing applications

The authors initially employs the fast Fourier transform (FFT) approach to transforming voice inputs into digital signals before integrating a speech recognition solution (which includes two models: the hidden Markov model (HMM) and the artificial neural network (ANN)). To achieve standard-tone identification of voice signals and digitally store speech, the authors then incorporated a 2048-bit Rivest-Shamir-Adleman (RSA) encryption method to encrypt and decrypt digital speech. The authors’ building team constructed the program using a 256-bit advanced encryption standard - Galois counter mode (AES-GCM) encryption method to assure the application’s effectiveness. The authors successfully created a voice recognition application according to the HMM of ANN. The collected findings suggest that the authors’ secure speech recognition program (named soft voice - RSA) has improved in terms of safety, keeping speech material secret, and speed. It takes roughly 0.2 s to generate a 2048-bit RSA key pair that exceeds the National Institute of Standards and Technology (NIST) standard, 700-1070 ms to process speech, 1-4 ms to encrypt 2048-bit RSA, 6-8 ms to decrypt 2048-bit RSA.

Visual Deprivation Alters Functional Connectivity of Neural Networks for Voice Recognition: A Resting-State fMRI Study

Humans recognize one another by identifying their voices and faces. For sighted people, the integration of voice and face signals in corresponding brain networks plays an important role in facilitating the process. However, individuals with vision loss primarily resort to voice cues to recognize a person's identity. It remains unclear how the neural systems for voice recognition reorganize in the blind. In the present study, we collected behavioral and resting-state fMRI data from 20 early blind (5 females; mean age = 22.6 years) and 22 sighted control (7 females; mean age = 23.7 years) individuals. We aimed to investigate the alterations in the resting-state functional connectivity (FC) among the voice- and face-sensitive areas in blind subjects in comparison with controls. We found that the intranetwork connections among voice-sensitive areas, including amygdala-posterior "temporal voice areas" (TVAp), amygdala-anterior "temporal voice areas" (TVAa), and amygdala-inferior frontal gyrus (IFG) were enhanced in the early blind. The blind group also showed increased FCs of "fusiform face area" (FFA)-IFG and "occipital face area" (OFA)-IFG but decreased FCs between the face-sensitive areas (i.e., FFA and OFA) and TVAa. Moreover, the voice-recognition accuracy was positively related to the strength of TVAp-FFA in the sighted, and the strength of amygdala-FFA in the blind. These findings indicate that visual deprivation shapes functional connectivity by increasing the intranetwork connections among voice-sensitive areas while decreasing the internetwork connections between the voice- and face-sensitive areas. Moreover, the face-sensitive areas are still involved in the voice-recognition process in blind individuals through pathways such as the subcortical-occipital or occipitofrontal connections, which may benefit the visually impaired greatly during voice processing.

Research Paper on Artificial Intelligence

This branch of computer science is concerned with making computers behave like humans. Artificial intelligence includes game playing, expert systems, neural networks, natural language, and robotics. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog. Artificial intelligence is working a lot in decreasing human effort but with less growth.

Addressing the selection bias in voice assistance: training voice assistance model in python with equal data selection

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistant to combat gender bias.

A Voice Recognition Based Game Design for More Accurate Pronunciation of English

The development of technology has made it considerably easier for people to meet a number of needs. Without technology, it is no longer possible to run certain applications. Nowadays, in many countries, the process of speaking English poses some problems in terms of different situations, such as allocating time for people. In this study, a game-based application was developed to help a person anywhere in the world pronounce English better. The application was implemented in C # programming language. The Speech.dll library was used to introduce voice commands to the system and to perform other necessary operations. Voice commands can be sent by the user via the wireless headset from anywhere in the shooting area. There is no need to wait at the computer while using the application because the developed application gives voice feedback to the user that it is right or wrong after the voice recognition process. In this application letter, word or sentence exercises can be done. The program aims to improve the level of pronunciation of people who want to improve their English speaking skills.

Design of an integrated learning approach to assist real-time deaf application using voice recognition system

Modern voice recognition is the process of predicting the human voice using emergent artificial intelligence techniques. It is extensively adopted in real-time applications for identity verification, helping deaf/dumb people, electronic voice eavesdropping, and hearing-impairment purposes. Predicting salient and discriminative process flow in the voice recognition process is the most challenging task in identifying the voice. Some existing research works find it complex to provide better prediction results. However, this work concentrates on modelling an efficient approach to predict the voice using deep learning approaches and it includes four essential phases: data acquisition phase, input voice pre-processing phase, word segmentation phase and classification. For experimentation purpose, an available online dataset was initially acquired and fed as an input to the proposed model. Subsequently, the provided input is pre-processed using an improved Mel-DCT filter for voice activity detection and to improve the classifier performance. The pre-processed image is then supplied into the word segmentation phase where the words from the input data are segmented using the Grab Cut segmentation. Finally, the classification was done with the integrated Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The integration is performed to handle the drawbacks of the single classifier model and to boost the prediction accuracy.

170 An Integrated Voice Recognition and Natural Language Processing Platform to Automatically Extract Thoracolumbar Injury Classification Score (TLICS) Features from Radiology Reports

INTRODUCTION: Many predictive models have been reported in the literature for clinical outcomes after spine surgery. However, the real-time implementation of these predictive scores in clinical practice is limited by the time intenstive nature of manually abstracting relevant predictors from the electronic health record. Natural language processing (NLP) provides a framework to automate data abstraction for commonly used predictive scores in spine surgery. METHODS: We retrieved and annotated the radiology reports of all Mayo Clinic patients with an International Classification of Diseases(ICD)-9/10 code corresponding to a fracture of the thoracolumbar spine between January 2005 and October 2020. Annotated data was used to train an n-gram NLP model using machine learning (ML) methods including random forest(RF), stepwise linear discriminant analysis (sLDA), k-nearest neighbors (kNN), and penalized logistic regression (pLR) models. RESULTS: 1,085 spine radiology reports were included in our analysis consististing of 868 to train our ML-NLP model and 217 for validation. Our dataset included 483 compression, 401 burst, 103 translational/rotational, and 98 distraction fractures. 103 reports had documented an injury of the PLC. The overall accuracy of the RF model for fracture morphology feature detection was 76.96% versus 65.90% in the stepwise LDA, 50.69% in the kNN, and 62.67% in the pLR. The overall accuracy to detect PLC integrity was highest in the RF model at 83.41% versus 70.97% in sLDA, 70.05% in kNN, and 67.74% in pLR model. Our RF model was implemented in the backend of a web application where users can dictate reports and have TLICS score features be automatically extracted. CONCLUSION: We have developed a robust ML-NLP model for extracting TLICS score features from radiology reports, which we deployed in a convenient web application that can be integrated prospectively into clinical practice and scaled to other commonly used predictive scores.

Research on the application of intelligent speech recognition technology in medical big data fog computing system

ABSTRACT Through the establishment of a voice database and voice recognition model, combined with voice recognition processing methods, an intelligent voice recognition input system with medical big data and fog computing as the scene is developed to realise the application of intelligent voice recognition technology in the system, which can reduce the medical staffdaily burden, reduce repetitive workload, and improve the quality and efficiency of clinical work. Based on the key technologies of intelligent voice recognition and medical big data, combined with the characteristics of fog computing, the development of electronic medical records and examination reports intelligent voice entry, mobile nursing intelligent voice entry, non-contact intelligent voice data interaction system, through the construction of fog computing model, design and develop an intelligent medical system to realise the rapid input and output of medical records and examination reports, reduce the workload of doctors and nurses, and concentrate their limited time on the treatment process itself. At the same time, according to the working characteristics of different medical departments, the intelligent system design adopts convenient, practical and efficient intelligent speech recognition technology application tools, and makes full use of big data technology and fog computing architecture to improve speech recognition rate, reduce network delay, and improve query efficiency, It is beneficial to assist the work of various clinical departments, better improve the treatment effect and innovate new medical models.

BGL Group: Artificial Intelligence (AI) Strategy

This research presents a detailed case analysis of BGL Group, a leading, international, distributor of insurance and household financial services. The AI strategy is described by analysing and evaluating a set of AI applications covering a variety of business areas: 1. Machine learning for pricing; 2. Chatbot AI technology to improve the customer experience in e-service; 3. Customer experience design thinking and a/b testing in new product development; 4. Voice recognition and Natural Language Processing (NLP) in call centre operations; 5. AI techniques for market segmentation. Each application is described in detail, and the concept of value creation in service markets is illustrated using data flow diagrams of customer interactions for different stages of the customer journey. A benefits matrix model is proposed that captures the principal AI benefits to both the supplier and the customer. The case discussion uses a new model, an AI systems map, to describe and explain the overall landscape of current AI applications, traditional Management Information Systems (MIS) and possible future application areas based on broad AI strategies and cognitive AI/thinking machines. Some concluding remarks are made on the importance of a digital first culture, up-to-date digital infrastructure and technology partnerships for successful implementation of AI systems, the crucial role of big data in AI strategies, and the growing importance of AI ethics in business applications. Finally, some propositions are offered regarding the future direction of AI in insurance markets.

Artificial intelligence and music: History and the future perceptive

Several music software programs have been developed that use AI to produce music. Like its applications in other fields, the A.I. in this case also simulates mental tasks. A prominent feature is the capability of the A.I. algorithm to learn based on information obtained such as the computer accompaniment technology, which is capable of listening to and following a human performer so it can perform in synchrony. Artificial intelligence also drives the so-called interactive composition technology, wherein a computer composes music in response to the performance of a live musician. There are several other A.I. applications to music that covers not only music composition, production, and performance but also the way it is marketed and consumed. Apart from programs that use AI to produce music, several music player programs have been developed to use voice recognition and natural language processing technology for music voice control.

An Artificial-Intelligence-Based Semantic Assist Framework for Judicial Trials

AbstractDue to their success in routine tasks such as voice recognition, image classification, and text processing, extensive attention has been aroused on how to use artificial intelligence (AI)-based automation tools in the judicial-trial process to improve efficiency. Meanwhile, judicial trial is a complex task that requires accurate insight and subtle analysis of the cases, law, and common knowledge. Applying the results provided by AI-based automation tools directly to the judicial-trial process is controversial due to their irregular logic and low accuracy. Based on this observation, this article investigates the logic underlined in judicial trials and the technical characteristics of AI, and proposes an AI-based semantic assist approach for judicial trials that is logical and transparent to the judges.

Personal Assistant Based Home Automation using Raspberry PI

A desired technology in 21st century for home automation is an intelligent system. An objective of any automated system is to reduce human labor, efforts, time and errors. Commonly Home Automation is a system where various home appliances like air conditioner, TV, Refrigerator, etc are capable to communicate with each other and they can be remotely controlled. Here is presented a paper which discuss a home automation system through virtual personal assistant’s capability of speech recognition and through raspberry pi to automate home devices such as air cooler, lights, fans, TV etc. This system is a combination of various different technologies such as voice recognition, voice analysis and voice processing. A device microphone is used to receive voice requests while the speaker gives voice output.. The system also uses Artificial intelligence techniques which can solve various human queries through web search and can also control the home appliances.

Voice recognition system for controlling electrical appliances in smart hospital room

Nowadays, most hospitals have new problem that is lack of medical nurse due to the number of patient increas rapidly. The patient especially with physical disabilities are difficult to control the switch on electrical appliances in patient’s room. This research aims to develope voice recognition based home automation and being applied to patient room. A miniature of patient’s room are made to simulate this system. The patient's voice is received by the microphone and placed close to the patient to reduce the noise.V3 Voice recognition module is used to voice recognition process. Electrical bed of patient is represented by mini bed with utilising motor servo. The lighting of patient room is represented by small lamp with relay. And the help button to call the medical nurse is represented by buzzer. Arduino Uno is used to handle the controlling process. Six basic words with one syllable are used to command for this system. This system can be used after the patient's voice is recorded. This system can recognize voice commands with an accuracy 75%. The accuracy can be improved up to 85% by changing the voice command into two syllables with variations of vowels and identical intonation. Higher accuracy up to 95% can be reached by record all the subject’s voice.