Natural Form Of Communication Research Articles

A Survey on Speech Emotion Recognition System using CNN Algorithm

The speech emotion recognition system (SER) plays an important role in decoding and predicting the speaker's emotional state by analyzing audio signals. Emotions are often simplified and grouped into categories such as anger, happiness, sadness, and even neutral emotional states. These emotions serve to communicate one's thoughts and provide insight into one's physical and mental health. Speech is the most basic and natural form of communication when interacting with others. Voice signals enable rapid communication between individuals, making them a valuable and effective method of expression. Over the past decades, countless research initiatives have been devoted to the development of voice-based automatic emotion recognition systems, especially to improve human-machine communication. Voice is gradually taking center stage in the field of Human-Machine interfaces in the IT sector. This interdisciplinary field draws on a variety of fields, including computer science, signal processing, psychology, linguistics, and more. As technology advances, it allows for seamless communication between humans and machines. Speech recognition not only interprets speech but also captures nuances in a person's tone and expressions, similar to body language. Therefore, it becomes an essential element of the Human-Machine communication system.

A Smart Device with Hand Gesture using Webcam

Abstract: Over time, Human-Computer Interaction (HCI) has experienced tremendous growth. An entirely new era in the AI domain of human-computer interaction was ushered in by hand gesture recognition technologies. One of the easiest and most natural forms of communication is hand gestures. It might be challenging for novice With hand gestures, the user can control cursor functions on the system. The input device for the suggested system is a camera. this application, HCI deployment for educational purposes will enter a completely new age.

A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory

Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.

Intelligent and Interactive Healthcare System (I2HS) Using Machine Learning

There has been a gigantic stir in the world’s healthcare sector for the past couple of years with the advent of the Covid-19 pandemic. The healthcare system has suffered a major setback and, with the lack of doctors, nurses, and healthcare facilities the need for an intelligent healthcare system has come to the fore more than ever before. Smart healthcare technologies and Artificial Intelligence (AI)/Machine Learning (ML) algorithms provide encouraging and favorable solutions to the healthcare sector’s challenges. An Intelligent Human-Machine Interactive system is the need of the hour. This paper proposes a novel architecture for an Intelligent and Interactive Healthcare System that incorporates edge/fog/cloud computing techniques and focuses on Speech Recognition and its extensive application in an interactive system. The focal reason for using speech in the healthcare sector is that it is easily available and can easily predict any physical or psychological discomfort. Simply put, human speech is the most natural form of communication. The Hidden Markov Model is applied to process the proposed approach as using the probabilistic approach is more realistic for prediction purposes. Ongoing projects and future work directions and challenges/issues are also addressed.

Sign To Text Conversion- Helping Aid

The ancient way of sign language is most natural forms of communication. The recognition of sign is place a key role in research field. The development and improvement on this kind of work need more and more new techniques to analyze the accurate results. Many people don't know it and interpreters are hard to come by, we developed a real-time technique for finger spelling-based American Sign Language using neural networks. In our technique, the hand is first sent through a filter, and then it is passed through a classifier, which analyses the class of hand movements. For each alphabet the proposed model has a 96 percent accuracy rate. This model mainly implemented for Dumb and Deaf people for communication.

Mapping Meaning and Purpose in Human-Robot Teams: Anthropomorphic Agents in Military Operations

We spontaneously tend to project animacy and sensitivity to inanimate objects and sometimes we attribute distinctively human features like intelligence, goals, and reasons to certain artificial devices. This phenomenon is called “anthropomorphism” and has been long studied by researchers in human-robot interaction and social-robotics. These studies are particularly important from the perspective of recent developments in military technology, as autonomous systems controlled by AI are expected to play a greater and greater role in the future of warfare. Anthropomorphistic effects can play a critical role in tactical operations involving hybrid human-robot teams, where service members and autonomous agents need to quickly coordinate relying almost exclusively on fast, cognitively parsimonious, natural forms of communication. These forms rely importantly on anthropomorphism to allow human soldiers read the behavior of machines in terms of goals and intentions. Understanding the cognitive mechanisms that underpin anthropomorphistic attributions is hence potentially crucial to increase the accuracy and efficacy of human-machine interaction in military operations. However, this question is largely philosophical, as numerous models compete in the space of social cognition theory to explain behavior reading and mental states attribution. This paper aims to offer an initial exploration of these mechanisms from a perspective of philosophical psychology and cognitive philosophy, reviewing the theories in social cognition that are most promising to explain anthropomorphism and predict how it can enable and improve natural communication between soldiers and autonomous military technologies.

Decoding Covert Speech From EEG-A Comprehensive Review.

Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition

Recent advances in imagined speech recognition from EEG signals have shown their capability of enabling a new natural form of communication, which is posed to improve the lives of subjects with motor disabilities. However, differences among subjects may be an obstacle to the applicability of a previously trained classifier to new users, since a significant amount of labeled samples must be acquired for each new user, making this process tedious and time-consuming. In this sense, unsupervised domain adaptation (UDA) methods, especially those based on deep learning (D-UDA), arise as a potential solution to address this issue by reducing the differences among feature distributions of subjects. It has been shown that the divergence in the marginal and conditional distributions must be reduced to encourage similar feature distributions. However, current D-UDA methods may become sensitive under adaptation scenarios where a low discriminative feature space among classes is given, reducing the accuracy performance of the classifier. To address this issue, we introduce a D-UDA method, named Standardization-Refinement Domain Adaptation (SRDA), which combines Adaptive Batch Normalization (AdaBN) with a novel loss function based on the variation of information (VOI), in order to build an adaptive classifier on EEG data corresponding to imagined speech. Our proposal, applied over two imagined speech datasets, resulted in SRDA outperforming standard classifiers for BCI and existing D-UDA methods, achieving accuracy performances of 61.02±08.14% and 62.99±04.78%, assessed using leave-one-out cross-validation.

Landline Natives: Telephone Practices since the 1950s as Innovation.

The telephone has played a key role in shaping modern life. While most scholars focus on the early use of landline telephones, this article follows the subsequent social history of landline telephones in the late twentieth century as an equally significant phase of innovation, when telephone practices changed radically as a result of transformations in national and household infrastructures. In this article, we identify a new generation of "landline natives" emerging around 1968; for them, the telephone was a natural form of communication and part of their home environments. Our case study of how telephone use became taken for granted serves as a prehistory for scholars studying cellphone and smartphone practices as well as media scholars seeking to understand audience participation in television and radio.

An efficient speech recognition system for arm‐disabled students based on isolated words

AbstractOver the previous decades, a need has emerged to empower human‐machine communication systems, which are essential to not only perform actions, but also obtain information especially in education applications. Moreover, any communication system has to introduce an efficient and easy way for interaction with a minimum possible error rate. The keyboard, mouse, trackball, touch‐screen, and joystick are all examples of tools which were built to provide mechanical human‐to‐machine interaction. However, a system with the ability to use oral speech, which is the natural form of communication between humans instead of mechanical communication systems, can be more practical for normal students and even a necessity for arm‐disabled students who cannot use their arms to handle traditional education tools like pens and notebooks. In this paper, we present a speech recognition system that allows arm‐disabled students to control computers by voice as a helping tool in the educational process. When a student speaks through a microphone, the speech is divided into isolated words which are compared with a predefined database of huge number of spoken words to find a match. After that, each recognized word is translated into its related tasks which will be performed by the computer like opening a teaching application or renaming a file. The speech recognition process discussed in this paper involves two separate approaches; the first approach is based on double thresholds voice activity detection and improved Mel‐frequency cepstral coefficients (MFCC), while the second approach is based on discrete wavelet transform along with modified MFCC algorithm. Utilizing the best values for all parameters in just mentioned techniques, our proposed system achieved a recognition rate of 98.7% using the first approach, and 98.86% using the second approach of which is better in ratio than the first one but slower in processing which is a critical point for a real time system. Both proposed approaches were compared with other relevant approaches and their recognition rates were noticeably higher.

How do medium naturalness and personality traits shape academic achievement and perceived learning? An experimental study of face-to-face and synchronous e-learning

This controlled experiment examined how academic achievement and cognitive, emotional and social aspects of perceived learning are affected by the level of medium naturalness (face-to-face, one-way and two-way videoconferencing) and by learners’ personality traits (extroversion–introversion and emotional stability–neuroticism). The Media Naturalness Theory explains the degree of medium naturalness by comparing its characteristics to face-to-face communication, considered to be the most natural form of communication. A total of 76 participants were randomly assigned to three experimental conditions: face-to-face, one-way and two-way videoconferencing. E-learning conditions were conducted through Zoom videoconferencing, which enables natural and spontaneous communication. Findings shed light on the trade-off involved in media naturalness: one-way videoconferencing, the less natural learning condition, enhanced the cognitive aspect of perceived learning but compromised the emotional and social aspects. Regarding the impact of personality, neurotic students tended to enjoy and succeed more in face-to-face learning, whereas emotionally stable students enjoyed and succeeded in all of the learning conditions. Extroverts tended to enjoy more natural learning environments but had lower achievements in these conditions. In accordance with the ‘poor get richer’ principle, introverts enjoyed environments with a low level of medium naturalness. However, they remained focused and had higher achievements in the face-to-face learning.(Published 13 July 2017)Citation: Research in Learning Technology 2017, 25: 1945 - http://dx.doi.org/10.25304/rlt.v25.1974

Speech therapy for children with dysarthria acquired before three years of age.

Children with motor impairments often have the motor speech disorder dysarthria, a condition which effects the tone, strength and co-ordination of any or all of the muscles used for speech. Resulting speech difficulties can range from mild, with slightly slurred articulation and breathy voice, to profound, with an inability to produce any recognisable words. Children with dysarthria are often prescribed communication aids to supplement their natural forms of communication. However, there is variation in practice regarding the provision of therapy focusing on voice and speech production. Descriptive studies have suggested that therapy may improve speech, but its effectiveness has not been evaluated. To assess whether any speech and language therapy intervention aimed at improving the speech of children with dysarthria is more effective in increasing children's speech intelligibility or communicative participation than no intervention at all , and to compare the efficacy of individual types of speech language therapy in improving the speech intelligibility or communicative participation of children with dysarthria. We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2015 , Issue 7 ), MEDLINE, EMBASE, CINAHL , LLBA, ERIC, PsychInfo, Web of Science, Scopus, UK National Research Register and Dissertation Abstracts up to July 2015, handsearched relevant journals published between 1980 and July 2015, and searched proceedings of relevant conferences between 1996 to 2015. We placed no restrictions on the language or setting of the studies. A previous version of this review considered studies published up to April 2009. In this update we searched for studies published from April 2009 to July 2015. We considered randomised controlled trials and studies using quasi-experimental designs in which children were allocated to groups using non-random methods. One author (LP) conducted searches of all databases, journals and conference reports. All searches included a reliability check in which a second review author independently checked a random sample comprising 15% of all identified reports. We planned that two review authors would independently assess the quality and extract data from eligible studies. No randomised controlled trials or group studies were identified. This review found no evidence from randomised trials of the effectiveness of speech and language therapy interventions to improve the speech of children with early acquired dysarthria. Rigorous, fully powered randomised controlled trials are needed to investigate if the positive changes in children's speech observed in phase I and phase II studies are generalisable to the population of children with early acquired dysarthria served by speech and language therapy services. Research should examine change in children's speech production and intelligibility. It must also investigate children's participation in social and educational activities, and their quality of life, as well as the cost and acceptability of interventions.

Mat Lab Based Synthesis of Speech & SpeakerReorganization Using Data Driven Approach

If you could talk to your Personal Computer then it would provide a comfortable and natural form of communication. It will reduce the typing amount, and it would be effortless, and allow you to move away from the terminal or screen. It is not necessary that you will not have to be in the line of sight of the terminal. As it will help you in many cases to tell you that who was speaking. If i want to use voice as a new medium on a computer workstation, it is natural explore how speech recognition can contribute to such an environment. Now we will review the state of speech and speaker recognition, focusing on current technology applied to personal workstations. The objective of this paper is to provide explanation of the speech and speaker recognition data input of the user. An algorithm of speech and speaker recognition which efficiently determines the optimum coordination of H.M.M has been successfully designed with the help of MATLAB processing.

Development of Isolated Numeric Speech Corpus for Swahili Language for Development of Automatic Speech Recognition System

ABSTRACT Speech corpus being the basic requirement for the development of Automatic speech recognition (ASR) system, it should be done with much accuracy in order to enhance the performance of the system. This paper describes the proposed procedure to abide while collecting the speech corpus of Swahili language from the native and non native speaker for the development of Automatic Speech Recognition system in Swahili language. General Terms Speech Database, Speech Recognition, Natural Language Processing, Human Computer Interaction Keywords Swahili, Swahili Text corpus, Phonetics, Text Corpus and Speech Corpus, Automatic Speech Recognition 1. INTRODUCTION Speech is the most prominent and natural form of communication between humans. Communication among the human being is dominated by spoken languages; therefore the researchers are trying to build the speech interfaces for computer [1]. Speech has potential of being used as a mode of interaction with computer. Human beings have long been motivated to create systems that can understand and talk like human. In this direction, researchers have tried to develop system for analysis and classification of the speech signals. Since, 1960s computer scientists have been researching ways and means to make computer record, interpret and understand human speech. Speech processing has become increasingly important in daily life, as the number of web enabled mobile phone users in rural as well as in urban area is increasing. Most of the researches efforts are in the field are of natural language processing (NLP) for African Language where Swahili is included and has majorly rooted in the rule based paradigm. The rule based approach has some merits as well as demerits. The merit of Swahili is in term of its design transparency and the demerits are that it’s highly language dependency and costly to develop as it typically involves a lot of manual effort of experts from Natural Language Processing field. unlike adaptations of the Arabic script for other languages, The system are decidedly competence based which is often tweaked and tuned towards a small sets of ideal sample words or sentences neglecting the real-world language technology application. In Language technologies for many African languages the researchers are getting tired of publication on real-world data or reports. Currently with the increased need of digital resource usage in the continent of Africa, there is a great need for more empirical approaches such as data driven and corpus based approach for language technologies. The main advantages of these approaches are: language impendence, development speed, robustness and empiricism. There is scarcity of sources in the sense that the digital text resources are few. The recent effort on the same is handled carefully with selected procedure for Swahili [2, 3]. For language technology applications such as speech recognition system, text-to-speech synthesis, machine aided translation and web related issues there is a great need for translation and usability of the Swahili language. There is a great need of work to be done in semantics and syntactic of Swahili language as the biggest online web Text resources which are available on Google, Yahoo and Wikipedia are not that correct. The major need is the extraction of information which enhances and refocuses on embarking on Swahili as a language, the corpus availability needs to be syntactically & semantically correct. This paper focuses on the procedure to be followed for the development of isolated numeric speech corpus. The information about Swahili language is described in the Section 2. Section 3 describes the Swahili text Corpus selected. Section 4 describes the procedure to be followed for developing the speech corpus. Section 5 describes the recording procedure to be followed. The Conclusion and the Future work are discussed in Section 6.

Indian Language Speech Database: A Review

is the most prominent and natural form of communication between humans. Human beings have long been motivated to create computer that can understand and talk like human. When the research tries to develop certain recognition system they require certain previously stored data i.e. database for respective recognition system. There are various speech databases available for European Language but very less for Indian Language. In this paper we discuss the various Speech Database developed in different Indian Languages for speech recognition system & Text to Speech System.

Infant signs as intervention? Promoting symbolic gestures for preverbal children in low-income families supports responsive parent–child relationships

Abstract Gestures are a natural form of communication between preverbal children and parents which support children's social and language development; however, low-income parents gesture less frequently, disadvantaging their children. In addition to pointing and waving, children are capable of learning many symbolic gestures, known as “infant signs,” if modeled by adults. The practice of signing with infants is increasingly popular in middle-income populations around the world, but has not been examined as an intervention to promote positive qualities of the parent–child relationship. This study tested whether an infant sign intervention (ISI) encouraging low-income parents to use symbolic gestures could enhance the parent–child relationship. A final sample of twenty-nine toddlers and their families were followed for 7 months after assignment to the ISI or a control group. Children and mothers in ISI group families used more symbolic gestures than those in control families. Mothers’ in the ISI group were more attuned to changes in children's affect and more responsive to children's distress cues. Mothers in the intervention group also viewed their children more positively, reducing parenting-related stress. This study provides evidence that a simple infant sign intervention is an effective tool to promote bidirectional communication and positive interactions for preverbal children and their parents.

An Educational Text-Dependent Speaker Recognition System

Speech is one of the most natural forms of communication. Recently there have been many developments and research in the field of speech processing. Speaker recognition, an important application of speech processing, is the process of determining the identity of a speaker by comparing speech produced by a speaker to a number of speech samples stored in a database. This paper describes the development of an educational text-dependent speaker recognition system. Speech processing systems are highly complex and teaching students this subject with the underlying technologies can be a challenging task. The aim of this work was to give a hands-on-experience to students via the development of a speaker recognition system that can be used in laboratory sessions. The developed system is based on MATLAB and can easily be used to teach the basic principles of speaker recognition, including the effects of noise on the recognition process.

Emotion Recognition Using Speech and EEG Signal A Review

years the research interest is improving in the field of human computer interaction. This paper focus on one of the aspect of human computer interaction in concern with, the recognition of emotion in a person with the help of Electroencephalogram (EEG) signals and speech. EEG uses an electrical activity of the neurons inside the brain. EEG machine is used for acquisition of the electrical potential generated by the neurons when they are active. The Brain cells communicate with each other by sending electrical impulses. Emotions allow people to express themselves beyond the verbal domain. Speech is the most natural form of communication. A much of work is been done in speech recognition in various languages. It is one of the components that closely related to emotions. Very less work has been carried out using combine aspects of speech, emotion and EEG. Thus this paper attempts to review the combine efforts of EEG brain signal and Speech to recognize the emotions in humans.

A pilot study on augmented speech communication based on Electro-Magnetic Articulography

Speech is the most natural form of communication for human beings. However, in situations where audio speech is not available because of disability or adverse environmental condition, people may resort to alternative methods such as augmented speech, that is, audio speech supplemented or replaced by other modalities, such as audiovisual speech, or Cued Speech. This article introduces augmented speech communication based on Electro-Magnetic Articulography (EMA). Movements of the tongue, lips, and jaw are tracked by EMA and are used as features to create hidden Markov models (HMMs). In addition, automatic phoneme recognition experiments are conducted to examine the possibility of recognizing speech only from articulation, that is, without any audio information. The results obtained are promising, which confirm that phonetic features characterizing articulation are as discriminating as those characterizing acoustics (except for voicing). This article also describes experiments conducted in noisy environments using fused audio and EMA parameters. It has been observed that when EMA parameters are fused with noisy audio speech, the recognition rate increases significantly as compared with using noisy audio speech only.

Audiovisual Integration of Speech in a Bistable Illusion

Visible speech enhances the intelligibility of auditory speech when listening conditions are poor [1], and can modify the perception of otherwise perfectly audible utterances [2]. This audiovisual perception is our most natural form of communication and one of our most common multisensory phenomena. However, where and in what form the visual and auditory representations interact is still not completely understood. Although there are longstanding proposals that multisensory integration occurs relatively late in the speech-processing sequence [3], considerable neurophysiological evidence suggests that audiovisual interactions can occur in the brain stem and primary sensory cortices [4, 5]. A difficulty testing such hypotheses is that when the degree of integration is manipulated experimentally, the visual and/or auditory stimulus conditions are drastically modified [6, 7]; thus, the perceptual processing within a modality and the corresponding processing loads are affected [8]. Here, we used a bistable speech stimulus to examine the conditions under which there is a visual influence on auditory perception in speech. The results indicate that visual influences on auditory speech processing, at least for the McGurk illusion, necessitate the conscious perception of the visual speech gestures, thus supporting the hypothesis that multisensory speech integration is not completed in early processing stages.