Voice System Research Articles

Research Paper on Desktop Assistant

Abstract: The main thing of Artificial intelligence (AI) is the consummation of natural dialogue between humans and machines. There are numerous IT companies have used the dialogue systems technology to establish various kinds of Virtual assistants grounded on their operations and areas for adding commerce between mortal and machine, similar as Microsoft’s Cortana, Apple’s Siri, Amazon Alexa, Google Assistant. Like Microsoft Cortana we've created our own virtual desktop assistant only for Government College Of Engineering Chandrapur (GCOEC) using python which is suitable to run on any windows operating systems. We use python as a programming language because it has a multitude of libraries which can be used to create Artificial Intelligence. Our version of desktop assistant is capable of recognizing voice and voice commands. Desktop Assistant are programs on digital device like PC that hear and respond to verbal commands. Example, a user can say, “Open GCOEC Website” and the assistant will answer with the sanctioned website of GCOEC. Desktop Assistants can change the way of life of the people in a different manner. Bias is starting to build up in the community as AI’s are getting smarter in their own way to interact with humans in an easy language. The Desktop Assistant is a program that can fete mortal voices and can respond via integrated voice system. This paper will define the working of Desktop assistant, their main problems and limitations. In this paper it's described that the system of creating a Desktop assistant without using pall services, which will allow the expansion of similar bias in the future

Speech data system and computer database design based on improved genetic algorithm

In the intelligent age, computers are required to help people complete simple daily work. Among them, computer voice databases and systems occupy a very important position in the field due to their wide application. In order to optimize the system design method, the application of IGA algorithm is proposed, and the performance of the model under the algorithm is compared and tested. The algorithm experiment shows that when the IGA objective function value is 34.4, there is no change, and the number of iterations is 100; Compared with the traditional genetic algorithm, the value of the optimal solution is always the minimum. Then the error of the optimal solution under different algorithms is compared and analyzed. It is found that the error of the optimal solution under IGA operation has the minimum value of 0.0079; The experiment of speech recognition efficiency shows that the speech recognition rate under the intervention of IGA algorithm has increased by 8%, and the overall efficiency is higher than 95%. It can be seen from the above results that IGA is helpful to the acquisition of voice database data, and improves the recognition efficiency. The feasibility of the method is high, which is of great significance to the development of China’s intelligent system industry. But at present, the overall progress of the voice system is still limited, so expanding research methods to apply to the field of voice system is still the next research direction that can be explored.

Development of speech recognition system for remote vocal music teaching based on Markov model

With the popularization of smart homes, car audio systems and various speech recognition software, speech recognition systems have gradually entered people’s sights and are favored by most users because of their practicability and accuracy. Cognition is an important interface for human–computer interaction. It will become a research focus in the field of artificial intelligence. It plays an important role in cultivating the basic characteristics of music and cultivating students’ interest in music, and vocal music teaching. Teaching traditional vocal music education to students is in the form of classrooms, such as vocal music, arrangement, and bel canto. The disadvantage is the lack of communication between the classroom and teachers and students. On the other hand, the development of Internet technology provides a new teaching method for traditional vocal music teaching and provides a network infrastructure for building a vocal teaching system platform. Therefore, this article provides a preliminary construction of a remote vocal music education platform by combining vocal music education with Internet technology. The remote audio and video training system is a complex and relatively large project with multiple functions and is to introduce important functions in this system. At the same time, register and log in to the remote voice and video implementation requirements and system functions, respectively, to realize functions such as video training and video-on-demand training.

VIRTUAL RECOGNITION USING ARTIFICIAL INTELLIGENCE

Voice recognition are programs on digital devices that listen and respond to verbal commands. A user can say, “What’s the weather?” and the voice Recognition will answer with the weather report for that dayand location. They could say, “Tell me a story,” and the Recognition will jump into a tale. Numerous gadgets are becoming more astute in their own particular manner to cooperate with human in a simple language. Programs that are capable of responding tohuman voices via an integrated voice system are known as desktop-based voice recognition. Voice assistants are a significant AI innovation that has the potential to significantly alter people’s daily lives. We reviewed studies that used voice assistants for various tasks in this context.

Control Mouse using Hand Gesture and Voice

Abstract: In Human Computer Interaction (HCI), the finest invention is the mouse. Even though a wireless mouse or the Bluetooth mouse is in demand today, it still lacks in many fields like cost and power. This paper, therefore, puts forward a modern approach to regulate the mouse movements using a real-time camera with the help of the hand. Our System aims to improve the recognition of human hand postures in a Human Computer Interaction application, reduce the time spent computing and improve user comfort related to human hand postures. We have developed an application for computer mouse control. Based on the proposed algorithm and selected hand feature, the application has good time-based performance. The user finds it easier to operate the system due to the proposed hand postures in combination with the voice assistant. Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system.

Characterization of Deep Learning-Based Speech-Enhancement Techniques in Online Audio Processing Applications.

Deep learning-based speech-enhancement techniques have recently been an area of growing interest, since their impressive performance can potentially benefit a wide variety of digital voice communication systems. However, such performance has been evaluated mostly in offline audio-processing scenarios (i.e., feeding the model, in one go, a complete audio recording, which may extend several seconds). It is of significant interest to evaluate and characterize the current state-of-the-art in applications that process audio online (i.e., feeding the model a sequence of segments of audio data, concatenating the results at the output end). Although evaluations and comparisons between speech-enhancement techniques have been carried out before, as far as the author knows, the work presented here is the first that evaluates the performance of such techniques in relation to their online applicability. This means that this work measures how the output signal-to-interference ratio (as a separation metric), the response time, and memory usage (as online metrics) are impacted by the input length (the size of audio segments), in addition to the amount of noise, amount and number of interferences, and amount of reverberation. Three popular models were evaluated, given their availability on public repositories and online viability, MetricGAN+, Spectral Feature Mapping with Mimic Loss, and Demucs-Denoiser. The characterization was carried out using a systematic evaluation protocol based on the Speechbrain framework. Several intuitions are presented and discussed, and some recommendations for future work are proposed.

Design and Application Research of Embedded Voice Teaching System Based on Cloud Computing

Deep changes are occurring in the components and forms of education as a result of the ongoing integration and development of emerging technologies like cloud computing, mobile computing, and artificial intelligence with teaching and learning, and the digital transformation of education is consistently being pushed to new heights. Simultaneously, China’s higher education has concurrently reached the stage of popularization. The digitalization of higher education is related to the development quality and value proposition of higher education and determines whether it can adapt to the needs of quality diversification, lifelong learning, training personalization, and governance modernization in the popularization stage. As a result, the current and future phases of China’s higher education reform call for accelerating the pace of higher education’s digital transformation and guiding the high-quality growth of higher education with digital innovation. The application potential of intelligent learning systems in higher education is becoming more and more clear in this context. In view of this, this work draws from previous research and experiences to build and implement an embedded voice teaching system based on cloud computing and a deep learning model to meet the development needs of the current digital transformation of higher education. On the one hand, the new system can well compensate for the flaws and shortcomings of the current teaching means in universities and realize the accompanying ubiquitous learning by relying on the powerful storage and computing capacity of the cloud computing platform. On the other hand, this study designs a set of voice recognition methods integrating HMM + LSTM to enhance the embedded voice system’s recognition performance, ultimately allowing for the voice recognition feature to be implemented in the pedagogical system. When it comes to processing audio signals, the hybrid model makes use of both the HMM’s robust time processing capability and the deep neural network’s robust characterization capability and generalization performance. As a result, the voice recognition rate, anti-interference performance, and noise robustness can all be significantly improved. Finally, the embedded voice system is put through its paces in an experimental setting to gauge its performance and functionality. The results of the tests demonstrate that the created hybrid model has high recognition accuracy and good noise immunity, which will be utilized as a foundation for the design and development of the final system. Meanwhile, the new system’s functional modules have achieved the expected results with good stability and reliability. Trial results gathered through interviews and questionnaires demonstrate that the new system significantly enhances the intelligence and adaptability of college teaching methods and is conducive to promoting the improvement of college students’ cultural literacy and innovation ability.

David L. Mathewson. Voice and Mood: A Linguistic Approach

David L. Mathewson. <i>Voice and Mood: A Linguistic Approach</i>

RETRACTED ARTICLE: Application of embedded voice and digital forensics system in financial cost management

RETRACTED ARTICLE: Application of embedded voice and digital forensics system in financial cost management

Designing for the Embedding of Employee Voice

Previous research on employee voice has sought to design technological solutions that address the challenges of speaking up in the workplace. However, effectively embedding employee voice systems in organisations requires designers to engage with the social processes, power relations and contextual factors of individual workplaces. We explore this process within a university workplace through a research project responding to a crisis in educational service delivery arising from the COVID-19 pandemic. Within a successful three-month staff-led engagement, we examined the intricacies of embedding employee voice, exploring how the interactions between existing actors impacted the effectiveness of the process. We sought to identify specific actions to promote employee voice and overcome barriers to its successful establishment in organisational decision-making. We highlight design considerations for an effective employee voice system that facilitates embedding employee voice, including assurance, bounded accountability and bias reflexivity.

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

In contemporary development, autonomous vehicles (AVs) have emerged as a potential solution for sustainable and smart transportation to fulfill the increasing mobility demands whilst alleviating the negative impacts on society, the economy, and the environment. AVs completely depend on a machine to perform driving tasks. Therefore, their quality and safety are critical concerns for driving users. AVs use advanced driver assistance systems (ADASs) that heavily rely on sensors and camera data. These data are processed to execute vehicle control functions for autonomous driving. Furthermore, AVs have a voice communication system (VCS) to interact with driving users to accomplish different hand-free functions. Some functions such as navigation, climate control, media and entertainment, communication, vehicle settings, vehicle status, and emergency assistance have been successfully incorporated into AVs using VCSs. Several researchers have also implemented vehicle control functions using voice commands through VCSs. If a situation occurs when AV has lost control due to malfunctioning or fault in the installed computer, sensors and other associated modules, driving users can control the AV using voice notes to perform some driving tasks such as changing speeds, lanes, breaking, and directing the car to reach a safe condition. Furthermore, driving users need manual control over AV to perform these tasks in some situations, like lane changing or taking an exit due to divergence. These tasks can also be performed with the help of voice commands using VCSs. Therefore, finding the exact voice note used to instruct different actuators in risk situations is crucial. As a result, VCSs can greatly improve safety in critical situations where manual intervention is necessary. AVs’ functions and quality can be significantly increased by integrating a VCS with an ADAS and developing an interactive ADAS. Now, the driver functions are controlled by voice features. Therefore, natural language processing is utilized to extract the features to determine the user’s requirements. The extracted features control the vehicle functions and support driving activities. The existing techniques consume high computation while predicting the user command and causing a reduction in the AVs’ functions. This research issue is overcome by applying the variation continuous input recognition model. The proposed approach utilizes the linear training process that resolves the listening and time-constrained problems and uncertain response issues. The proposed model categorizes the inputs into non-trainable and trainable data, according to the data readiness and listening span. Then, the non-distinguishable data were validated by dividing it into the linear inputs used to improve the response in the AVs. Thus, effectively utilizing training parameters and the data decomposition process minimizes the uncertainty and increases the response rate. The proposed model has significantly improved the exact prediction of users’ voice notes and computation efficiency. This improvement enhances the VCS quality and reliability used to perform hand-free and vehicle control functions. The reliability of these functions ultimately improves the safety of AVs’ driving users and other road users.

Locative Imperatives in Javanese

Javanese is a member of the Western Austronesian language, a sub-family of the Austronesian language family. Languages in this sub-family are known in linguistic literature as having an exceptional feature in their voice system among the world’s languages and continue to be exciting issues in the theory of syntax. This paper investigates the voice system in Javanese and focuses on the locative imperative clauses’ voice system. Data are collected from the Javanese spoken in Yogyakarta at the ngoko level. This study demonstrates that Javanese has five variants of locative imperative clauses: intransitive locative imperatives, active locative imperatives, passive locative imperatives, locative imperatives with actor focus, and locative imperatives with locative focus. The five variants of imperatives reflect the voice system of the clauses. There are two voice systems of locative imperatives in Javanese: a two-voice system and a multiple-voice system. Intransitive locative imperatives, active locative imperatives, and passive locative imperatives are imperatives within the two-voice or active-passive voice systems. The other two variants of locative imperatives, the locative imperatives with actor focus and the locative imperatives with locative focus, are types of locatives imperatives within the multiple voice system. The existence of the two voice systems in Javanese indicates that this language is in the process of changing from a multiple-voice language to a two-voice language.

Laryngeal realism and the voicing contrast in Khuzestani Arabic stops

In laryngeal realism (LR), laryngeal specification of stops is explained by direct maps of cues (e.g. VOT) onto privative phonological laryngeal features [voice] or [spread glottis]. Phonetic realization of the segments and speakers’ ‘control’ (e.g. the degree of intervocalic voicing and speech rate manipulation effects on VOT duration) are used as diagnostics of phonological specification. Similar to some Arabic vernacular dialects (e.g. Qatari Arabic), Khuzestani Arabic in Iran presents a case where three voiced stops /b d ɡ/ are in contrast with voiceless stops /p t k/, but two voiceless guttural plosives /tˁ q/ have no voiced homorganic counterparts. In this paper we examine the phonetic realization of voicing in these stops at word-initial and intervocalic position, as well as the effects of speech rate manipulation on VOT and closure voicing. The data came from 12 native speakers recorded in Khorramshahr, Iran. Our findings suggest an over-specified voicing system in this Arabic variety. We found that voiced /b d ɡ/ were produced with voicing lead in initial position and complete closure voicing word medially, voiceless /p t k/ had long lag VOT, while guttural /tˁ q/ had short lag VOT. Speech rate manipulation revealed that only duration of (pre)voicing and duration of aspiration increased in slower speech. Also, f0, F1, and F2 were measured at vowel onset to evaluate the glottal state in production of stops. The results support the predictions of LR that voiced stops are specified by [voice], voiceless stops are [sg], while gutturals lack underlying specification for voice.

Application of Embedded Voice And Digital Forensics System In Financial Cost Management In Biotech Companies.

Financial cost management is a critical component of business operations in the biotech industry. Embedded voice and digital forensics systems are emerging technologies that can improve the efficiency and effectiveness of financial cost management. This abstract explores the application of embedded voice and digital forensics systems in financial cost management in biotech companies. The study highlights the potential of these technologies to enable real-time monitoring and analysis of financial transactions, enhance fraud detection and prevention, and optimize financial cost management. The study reviews the various applications of embedded voice and digital forensics systems in biotech companies, such as invoice processing, expense tracking, and vendor management. The study examines the benefits of using these technologies in financial cost management, such as improved accuracy and efficiency, reduced costs, and enhanced data security. The study also discusses the challenges of implementing embedded voice and digital forensics systems in biotech companies, such as data integration, system interoperability, and organizational resistance to change. The study concludes that embedded voice and digital forensics systems are valuable tools for financial cost management in biotech companies, enabling real-time monitoring and analysis of financial transactions, enhancing fraud detection and prevention, and optimizing financial cost management. The integration of these technologies in biotech companies can improve operational efficiency, reduce costs, and enhance data security, ultimately contributing to the growth and success of the biotech industry.

AN ARTIFICIAL INTELLIGENCE-BASED SPEAKING SYSTEM FOR DUMB PEOPLE

For deaf-mute people all around the world, Gestures are the main mode of communication for DeafMute people worldwide. In this gesture-based voice system, machine learning presents a real-time vision-based system for monitoring hand finger gestures. It was developed in Python using Raspberry Pi along with a camera module, which is compatible with the Open CV library for computer vision. The Raspberry Pi includes an image processing technique that monitors the fingers of the hand using the extracted attributes. The main purpose of a gesture based speaking system is to develop control communication between humans and computers. This leads to a system that can recognize and monitor known objects and has surveillance and application capabilities. The major goal of the suggested work was to allow the system to function properly. The main objective is to enable the system to recognize and monitor certain properties of objects specified by the Raspberry Pi along with camera module using an appropriate image processing technique. The Open CV library's feature extraction technique of the Open CV library for Python programming runs on Raspberry Pi using an external camera. A gesture based speaking system using machine learning provides a new, intuitive, and simple way to communicate with computers that are more human-like.

The Role of Artificial Intelligence in Voice-Based Systems: A Comparative Analysis of SAARUSH

In today’s market, AI is taking place globally in the world. The AI and voice system are published multiple times in various movies like IRON-MAN, SKYSCRAPER, etc. Unlike the original origin, artificial intelligence was developed as a system that can think like a human or better than a human and can help directly or indirectly to develop, learn, and perform system-oriented operations like in the movie of Iron-Man is a version of the AI called Jarvis which is an intelligent computer that can perform the system-oriented operations that converses in a large scale with the humans, the AI monitors his lifestyle and helps to build its computer program for his superhero suit. The SAARUSH AI system was inspired by the Jarvis AI system. Saarush is a digital life assistant that primarily leverages human communication methods which establish two-way contact with humans and aid them with chores such as cooking, facebook alerts, and many more. Because we mostly communicate by voice in our project, Saarush is essentially a speech recognition program. The term "speech technology" refers to two technologies: synthesizers and recognizers. The human voice is a signal that contains an endless quantity of data. Jarvis is the name of Tony Stark's assistant in the Superhero films. This one is frequently the result of multiple contributors' overcontribution, such as the usability but also the capability of AIML dynamic merging with portals such as Python [pyttsx] but instead gTTS, which results in the same JARVIS standard, structure with general reusability but little or no maintainability.

The rise of middle voice systems

AbstractMiddle markers are characterized by a distribution halfway between grammar and the lexicon: with some verbs, middle marking encodes valency change, while with others it obligatorily occurs with no obvious synchronic motivation. Despite the existing cross-linguistic work on middle markers, their history is still largely unknown. In the typological literature, the standard view is that middle markers predominantly have their origin in reflexive markers, and that, in their development, it is invariantly the grammatical component that expands to the lexical component. In this paper, I challenge these assumptions based on the analysis of a sample of 129 middle marking languages. As I show, the sources and pathways whereby middle markers come about are much more numerous and varied than what has been reported in the literature. By taking a source-oriented approach, I also discuss how recurrent cross-linguistic trends in the distribution of middle markers can in part be explained by looking at their history.

Retracted: Cloud‐Based Android Intelligent Voice and Enterprise Economic Statistics System Design

Retracted: Cloud‐Based Android Intelligent Voice and Enterprise Economic Statistics System Design

A Grammar Sketch of Southern Sinama Language

This study aimed to sketch the grammar of the Southern Sinama language, particularly that of the Simunul variety. The language is spoken in the island municipality of Simunul in Tawi-Tawi, Philippines, by some 34,000 people. This study employed a qualitative research method utilizing the descriptive design. Drawn from the elicited data, the description sketches the language in three different levels: phonology, morphology, and syntax. In the language phonology, significant features include 24 phonemes: 17 consonants and seven vowels; no consonant cluster occurs within syllable; word-initially, [m], [n], and [l] can occur as phonetically lengthened to the extent that they form a geminate cluster; and the replacement of segment is evident through nasal fusion. In language morphology, noted features comprise affixes which interrelate with other structures of the language, such as aspect, mood, and the voice system of the verbs, which in consequence, affects the meaning of the utterance. In terms of syntax, Southern Sinama is a head marking language with VSO word order whose clause structures encompass one to three arguments (i.e. actor, object, and benefactor).

Deep Learning-Based Optimization of Visual–Auditory Sensory Substitution

Visual–auditory sensory substitution systems can aid blind people in traveling to various places and recognizing their own environments without help from others. Although several such systems have been developed, they are either not widely used or are limited to laboratory-scale research. Among various factors that hinder the widespread use of these systems, one of the most important issues to consider is the optimization of the algorithms for sensory substitution. This study is the first attempt at exploring the possibility of using deep learning for the objective quantification of sensory substitution. To this end, we used generative adversarial networks to investigate the possibility of optimizing the vOICe algorithm, a representative visual–auditory sensory substitution method, by controlling the parameters of the method for converting an image to sound. Furthermore, we explored the effect of the parameters on the conversion scheme for the vOICe system and performed frequency-range and frequency-mapping-function experiments. The process of sensory substitution in humans was modeled to use generative models to assess the extent of visual perception from the substituted sensory signals. We verified the human-based experimental results against the modeling results. The results suggested that deep learning could be used for evaluating the efficiency of algorithms for visual–auditory sensory substitutions without labor-intensive human behavioral experiments. The introduction of deep learning for optimizing the visual–auditory conversion method is expected to facilitate studies on various aspects of sensory substitution, such as generalization and estimation of algorithm efficiency.