Voice Input Research Articles

Design a Chatbot for Chennai Corporation Using Logistic Regression Algorithm

Chatbots are the famous nowadays in business because of its service offered to the community at large. They provide support of 24*7 for business in terms of customercare, helpline, planning, analyzing and decision making. In this paper, a chatbot for Chennai corporation is proposed. This chatbot helps the citizens in providing the responses for their queries related to civic problems. There is no such system is available to handle the public grievances automatically. This system handles the public query and the relevant suggestion and responses will be given promptly. The chatbot receives the text or voice input and processed. The voice recognition module used to recognize the voice query and the voice to text convertor used to convert the voice data into text format. The matchmaking process used to match the input query with the available data set and the relevant responses is generated. If no match is for the query, the matchmaker will find the relevant response from online sources. The output channel equipped with the text to voice converter which converts the text data into voice and it will be delivered to the end user. The naïve bayers and logistic regression algorithm is implemented for classifying the query and the performance is compared. The result shows that the logistic regression algorithm outperform well with the precision and recall values.

Effect of Two Common Head-Mounted Augmented Reality Systems on Muscle Force and Blink Rate of Electric Utility Power Plant Operators

Augmented reality (AR) is a technology that combines real and virtual information presented to the user in an interactive way in real time. The Microsoft HoloLens and RealWear HMT-1 are two common types of head-mounted AR available to industrial field workers. These two AR systems were tested on how they affected blink rate, an indicator of eye strain, and electromyographic (sEMG) activity of the neck and shoulder muscles of electric utility power plant operators while they performed five routine inspection tasks of coal equipment. The inspection tasks were conducted under three conditions: HoloLens, HMT-1, and No AR (regular method). Workers communicated with the HoloLens with a right-hand gesture and by voice input to the HMT-1. The duration of the inspection tasks ranged from an average of 10 to 28 sec. Twelve experienced power plant operators participated in the study. sEMG) of the right and left sternocleidomastoid, splenius, semispinalis capitis, and upper trapezius muscles were measured, and a small camera recorded blink rate of the right eye. Results reveal generally no significant differences in 50th and 90th percentile sEMG between the three conditions for all eight muscles. Although the means of blink rate appeared consistently lower with the HoloLens than the No AR and HMT-1 (approximately 4.4 blinks/min), these differences were not significant at the 0.05 level (p=0.06 to 0.17). Future studies should investigate a larger sample size of workers wearing AR devices for longer time periods (> 30 min) to determine long-term effects of AR devices on muscle activity and eye strain. A system of hardware, software, and experimental protocol was developed that follow-up studies may employ to test physiological effects of AR devices.

A Hearing Device With an Adaptive Noise Canceller for Noise-Robust Voice Input

This paper presents a hearing device with an adaptive noise canceller for noise-robust voice input. The hearing device has an inner-ear and an outer-ear microphone to collect ear-canal speech signal and the outer-ear environmental signal, respectively. The environmental signal drives an adaptive filter to generate a noise replica which is subtracted from the inner-ear microphone signal to cancel the noise. Coefficients of the adaptive filter are updated by an NLMS (normalized least mean square) algorithm with an SNR-controlled stepsize for small speech distortion. The adaptive noise canceller is realized on a digital signal processor chip with 39 McPS (million cycles per second) for 64 taps. Evaluation results in speech recognition show that a word recognition rate of 89% is achieved when the noise level is 80 dBA with 38% improvement over the inner-ear signal without adaptive noise cancellation. DSP (digital signal processor) implementation details and two prototype devices demonstrate high feasibility.

Interaction 3D Human Anatomy of Ear, Nose, Throat Using Mix Reality

The development of information technology in the future will continue to grow and full of innovations, all shortcomings in an application or equipment will continue to be minimized, all ways to simplify and efficiently a technology are continuously looked for and developed by experts in their respective fields. The frequent use of 3D objects and animation in medical learning media is felt to be incomplete without the direct interaction of users with modern computers so that users are more interested in learning for Ear, Nose and Throat. Therefore, in this study, an interaction system was made on the Ear Nose Throat medical learning application using Microsoft HoloLens. In this study used interactions that use hand gesture and voice input. The results of this study, users will be able to interact with the Ear, Nose and Throat using Microsoft HoloLens. With this extensive application, it will facilitate users in Ear, Nose and Throat medical learning more interactively with the unique features of applications that use voice input and hand gesture.

Classification of Psychogenic and Laryngeal Voice Diseases Based on Teager Energy Operator

Among several ways of communication, the voice remains the fastest natural tool for human-to-human and human-to-machine communication. That is why the research in automatic voice pathology detection and classification area has gained much interest in the recent years. Indeed, these automatic systems may be considered as assistive tools for the physicians during the assessment stage. This latter may help them to make decision, whether the voice signal belongs to a healthy or unhealthy subject and identifies the nature of pathology. In this context, this paper provides a voice pathology detection and classification system based on wavelet analysis and Teager Energy Operator (TEO). First, we used the input voice signal that we taken from Saarbrücken Voice Database (SVD) [1], to extract a set of features. These feature vectors are fed into a Gaussian Mixture Model (GMM) [2] for the sake of classification. The obtained results are 96.66% for the detection task and 92.5 % using TEO. These results show that our proposal outperforms some state-of-art methods used in voice pathology identification.

ProxiTalk

Speech input, such as voice assistant and voice message, is an attractive interaction option for mobile users today. However, despite its popularity, there is a use limitation for smartphone speech input: users need to press a button or say a wake word to activate it before use, which is not very convenient. To address it, we match the motion that brings the phone to mouth with the user's intention to use voice input. In this paper, we present ProxiTalk, an interaction technique that allows users to enable smartphone speech input by simply moving it close to their mouths. We study how users use ProxiTalk and systematically investigate the recognition abilities of various data sources (e.g., using a front camera to detect facial features, using two microphones to estimate the distance between phone and mouth). Results show that it is feasible to utilize the smartphone's built-in sensors and instruments to detect ProxiTalk use and classify gestures. An evaluation study shows that users can quickly acquire ProxiTalk and are willing to use it. In conclusion, our work provides the empirical support that ProxiTalk is a practical and promising option to enable smartphone speech input, which coexists with current trigger mechanisms.

1B1-3 Safety in case of simultaneous execution of in-vehicle equipment operation and hazard detection by in-vehicle displays -Influence of display proximity,tactile warning and voice input on safety-

1B1-3 Safety in case of simultaneous execution of in-vehicle equipment operation and hazard detection by in-vehicle displays -Influence of display proximity,tactile warning and voice input on safety-

Automatic Lock of Cursor Movement: Implications for an Efficient Eye-Gaze Input Method for Drag and Menu Selection

This study proposed a method—automatic lock of cursor movement (ALCM)—that locks a cursor at the center of a target at the instant the cursor enters the target. The method is intended to suppress irritating subtle cursor movements that occur when an eye-gaze input system transforms involuntary eye movement (e.g., drift) into cursor coordinates. The effectiveness of the proposed ALCM was verified using pointing performance (speed and accuracy) in two types of HCI tasks. In a drag task, we compared mouse input versus eye-gaze input with use of a backspace (BS) key or voice input. The key or voice facilitates target selection once the eye gaze was aligned with a target. In a menu selection task, we also compared mouse input with eye-gaze and the use of two voice input conditions. This task required gaze alignment with a menu and menu item by use of voice input for selection. Whether the ALCM function was added to the eye-gaze input system or not was a within-subject factor. The input method and target sizes were within-subject factors. The study concluded that the ALCM improved pointing accuracy for all eye-gaze input methods and all two tasks.

Design and Implementation of VR Multi-Dimensional Intelligent Tourism Information System

Intelligent tourism is the inevitable trend of tourism development. It perfectly integrates computer information technology and tourism to provide users with a good experience. With the help of three-dimensional computer graphics, wide-angle (wide-field) stereo display technology, user tracking technology, stereo technology, tactile/mechanical feedback technology, voice input and output technology, VR-based multi-dimensional intelligent tourism system provides personalized services for users both online and offline. It is further changing the existing traditional access mode of scenic spots, providing double-line browsing access mode, both online and offline. It is enlarging the overall popularity of the scenic area, at the same time, improving the tourist reception capacity and service level, and providing more and more fun experience for tourists.

Using voice input for improving the efficiency of surveying plant disease severity

Using voice input for improving the efficiency of surveying plant disease severity

Voice Recognition Systems in the Cloud Networks

Voice recognition software enables computer users to use keyboards instead of only entering text using their voices. The medical and legal communities have both reported some success with speech recognition technology, even though the library literature is relatively mute on voice recognition technology. Voice-recognition technology can take over typing for someone who cannot do so due to a physical disability. Voice recognition may still be in its infancy, but it is advancing quickly and becoming more accurate, and it is well worth the investment of money, time, and effort required to learn it. Speech recognition artificial intelligence apps have seen a significant increase in numbers in recent years. Businesses increasingly rely on digital assistance and automated support to rationalize their offerings. Voice assistants, intelligent home appliances, search portals, and similar technologies are only a few instances of the widespread use of voice recognition. The research investigates the theory that one of the primary benefits of using a speech recognition system is th.at it enables the user to continue working on other tasks simultaneously. The user can direct their attention to observation and manual activities while maintaining control of the device using voice input commands. In addition, the study's findings indicate that voice recognition is an additional type of speech recognition in which a source sound is identified and matched to an individual's voice. For instance, Apple's Siri and Google's Alexa use AI-mechanized voice recognition to provide speech or text back support. On the other hand, voice-to-text programs such as Google Dictation translate words that are dictated to them as text.

Voice Recognition Systems in the Cloud Networks: Has It Reached Its Full Potential?

Voice recognition software enables computer users to use keyboards instead of only entering text using their voices. The medical and legal communities have both reported some success with speech recognition technology, even though the library literature is relatively mute on voice recognition technology. Voice-recognition technology can take over typing for someone who cannot do so due to a physical disability. Voice recognition may still be in its infancy, but it is advancing quickly and becoming more accurate, and it is well worth the investment of money, time, and effort required to learn it. Speech recognition artificial intelligence apps have seen a significant increase in numbers in recent years. Businesses increasingly rely on digital assistance and automated support to rationalize their offerings. Voice assistants, intelligent home appliances, search portals, and similar technologies are only a few instances of the widespread use of voice recognition. The research investigates the theory that one of the primary benefits of using a speech recognition system is that it enables the user to continue working on other tasks simultaneously. The user can direct their attention to observation and manual activities while maintaining control of the device using voice input commands. In addition, the study's findings indicate that voice recognition is an additional type of speech recognition in which a source sound is identified and matched to an individual's voice. For instance, Apple's Siri and Google's Alexa use AI-mechanized voice recognition to provide speech or text back support. On the other hand, voice-to-text programs such as Google Dictation translate words that are dictated to them as text.

Implementasi Google Speech API Pada Aplikasi Koreksi Hapalan Al-Qur'an Berbasis Android

Muroja’ah is a method of repeating new memorization and old memorization that be heard to other people. This method is very popular in Indonesia. In order to make the learning process isn’t boring, it is required a learning media that can be used anytime, one of them are using Android application. The research describes how to build a Qur’an recitation correction application based on Android and integrate it with Google Speech so that application built more interesting and interactive with users. Google Speech be integrate as voice input media, users requested recite Qur’an verses then Google Speech will convert their voice to text. Text result obtained then matched with text in source code. Result of testing the accuracy of Google Speech implementation on this application it’s 100%, that means it’s been as expected.

Code talkers

Using voice input to write programs.

Voice Controlled Home Automation

The work is mainly concentrated on the implementation of Voice Controlled Home Automation. It is all about the development of home appliances based on the voice command using Android. The system is used to support elderly and disabled people at home. Google application is used as voice recognition and process the voice input from the smart phone. The voice activated home automation is implemented using NodeMCU, relays, pcb connectors, diodes and power supply. The voice input is captured by the android and the NodeMCU receives the signal to control the light, fan, bulb

Medical Assistant Chat-Bot for Health Care Application

IoT revolution is re-designing modern health care with high technological, economic, and social prospects. Artificial intelligence (AI) aims to substitute human cognitive functions. It gives a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. The internet of things has lots of applications in healthcare, from remote monitoring to smart sensors and medical device integration. It has the ability to not only keep patients safe and healthy but to improve how physicians deliver care as well. Healthcare IoT can also boost patient engagement and satisfaction by allowing patients to spend more time interacting with their doctors. With the combination of both IoT & AI technologies, it can apply chatbots for medical assistance in healthcare. An IoT based monitoring system & AI based analytics system with an interactive chat-robots is the more outstanding application in healthcare. The Medical Assistant recognizes the user voice input and converts the speech into text. Here we concentrate on the different type of fevers, like chickenpox, malaria, septicemia, viral fever etc. Each fever has different symptoms .we finalize the fever by using symptoms. After that text mining, those phrases would be split as a noun and medical terms. From term analysis, the assistant will answer the query from users. It also analyzes the sensor data (body temp, heartbeat) from the cloud and expresses the user health condition.

Young Children and Voice Search: What We Know From Human-Computer Interaction Research.

Young children are prolific question-askers. The growing ubiquity of voice interfaces (e.g., Apple’s Siri, Amazon’s Alexa), as well as the availability of voice input in search fields, now make it possible for children to ask questions via Internet search when they are able to speak clearly, but before they have learned to read and write, typically between 3 and 6 years of age. The prevalence of voice search makes it important to understand children’s changing conceptions of digital devices as a source of information and the role of technology-mediated question-asking in development. While limited research has focused on young children’s use of voice interfaces, reviewing two related bodies of literature sheds light on how this use might unfold. This paper brings together studies of how children look for information, and of how they perceive and understand the informational and social roles of technology, drawing on human-computer interaction research. We conclude by highlighting lines of questioning for future work on younger children’s interaction through voice search.

Analisis Bentuk Pola Suara Menggunakan Ekstraksi Ciri Mel-Frequencey Cepstral Coefficients (MFCC)

Sound is a part of the human body that is unique and can be distinguished, so its application can be used in sound pattern recognition technology, one of which is used for sound biometrics. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study uses the Melf-Frequency Cepstrum Coefficients (MFCC) method for feature extraction process from speaker speech signals. The MFCC process will convert the sound signal into several feature vectors which will then be displayed in graphical form. Analysis and design of sound patterns using Matlab 2017a software. Tests were carried out on 5 users consisting of 3 men and 2 women, each user said 1 predetermined "LOGIN" word, which for 15 words said. The results of the test are the form of a sound pattern between the characteristics of 1 user with other users. Keywords—Voice, Pattern, Feature Extraction, MFCC

A Multimodal Interface for Virtual Character Animation Based on Live Performance and Natural Language Processing

ABSTRACTVirtual character animation is receiving an ever-growing attention by researchers, who proposed already many tools with the aim to improve the effectiveness of the production process. In particular, significant efforts are devoted to create animation systems suited also to non-skilled users, in order to let them benefit from a powerful communication instrument that can improve information sharing in many contexts like product design, education, marketing, etc. Apart from methods based on the traditional Windows-Icons-Menus-Pointer (WIMP) paradigms, solutions devised so far leverage approaches based on motion capture/retargeting (the so-called performance-based approaches), on non-conventional interfaces (voice inputs, sketches, tangible props, etc.), or on natural language processing (NLP) over text descriptions (e.g., to automatically trigger actions from a library). Each approach has its drawbacks, though. Performance-based methods are difficult to use for creating non-ordinary movements (flips, handstands, etc.); natural interfaces are often used for rough posing, but results need to be later refined; automatic techniques still produce poorly realistic animations. To deal with the above limitations, we propose a multimodal animation system that combines performance- and NLP-based methods. The system recognizes natural commands (gestures, voice inputs) issued by the performer, extracts scene data from a text description and creates live animations in which pre-recorded character actions can be blended with performer’s motion to increase naturalness.

Improving Usability Perception of Error-Prone AI Speakers: Elaborated Feedback Mitigates Negative Consequences of Errors

ABSTRACT Users of voice user interface (VUI) often encounter errors, such as when a VUI attempts to recognize a user’s voice inputs or execute tasks. Conversation is prone to errors, and in the collaborative perspective, communicators manage common ground together to handle erroneous situations. Adopting a collaborative view of conversation, we propose that a VUI can address different types of errors by providing users with feedback to aid them in developing common ground to communicate more effectively. To test this proposal, we conducted a 2 (error type: recognition vs. execution error) × 2 (feedback elaboration: present vs. absent) mixed-design experiment in which users interacted with a VUI speaker and evaluated its usability in these four modes. Participants reported greater acceptance of feedback and higher usability perception for a speaker returning execution errors than for one returning recognition errors, particularly when the speaker presented feedback articulating reasons for the errors. This finding indicates that a VUI can employ feedback explaining the causes of errors to facilitate the development of common ground and to minimize the negative consequences of errors.