scholarly journals CAMPUS MITHRA: Design and implementation of voice based attender robot

2021 ◽  
Vol 2115 (1) ◽  
pp. 012006
Author(s):  
Ambreen Saniya ◽  
M S Chandana ◽  
Maria Sharon Dennis ◽  
K Pooja ◽  
D J Chaithanya ◽  
...  
Keyword(s):  
Natural Language ◽  
Voice Recognition ◽  
Embedded Control ◽  
Railway Stations ◽  
Human Voice ◽  
Domains Of Life ◽  
The University ◽  
The Voice ◽  
University Setting ◽  
Line Following

Abstract A robot is a machine which is programmed by a computer and the movements and functions of which are controlled by an external or an embedded control. It has dynamic uses in all domains of life. A robot in a university setting can be used as an attender for passing circulars around instead of multiple attenders doing the task manually which is a cost and time consuming process. Parents often find it difficult to navigate through the unfamiliar university. In this paper, we have focused on a voice based attender robot with line following capabilities along with speech recognition that can be used at universities for a variety of purposes like passing around the circulars, interacting with parents and helping them navigate through the university through Spoken Natural Language. The main objectives of the proposed work is to reduce the burden of passing circulars, calling a student/faculty on the attender by designing a robot that is also competent enough to connect with human through spoken natural language such as English or Kannada, so that it interacts with parents who are new to the institution and do not know whom to approach. The main aim of this work is to introduce a robot that it is able to interact with human through the Spoken Natural Language. Here, the focus is on two languages; English and Kannada. This system uses a voice recognition module to recognize human voice and a voice playback module is used to reply back in either English or Kannada according to the user’s command. It can work in two modes, the voice recognition mode to answer to user queries or in line following mode to pass circulars, call student/faculty. In this way, the voice based attender robot finds its applications in the university setting. But it is not limited to only universities. It can also be further implemented in places like railway stations, bus stations, big factories and other similar surroundings.

Implementation of Attender Robot in Campus

2021 ◽  
Vol 1 (2) ◽  
pp. 1-8
Author(s):  
Chaithanya D.J ◽  
Ramya B ◽  
Aisiri A.P ◽  
Spoorthi S.P
Keyword(s):  
Natural Language ◽  
Review Paper ◽  
Text Recognition ◽  
Embedded Control ◽  
Human Speech ◽  
Wide Range ◽  
The Voice ◽  
University Setting ◽  
Line Following ◽  
Numerous People

A robot is a machine that has been programmed by a computer and has external or embedded control over its movements and activities. It has a wide range of applications in all aspects of life. A robot can be employed as an attender in a university setting to distribute circulars around instead of numerous people doing it manually, which is a costly and time-consuming method. The unfamiliar campus might be challenging for parents to navigate. In this review paper, we focus on a voice-based attender robot with line-following skills and speech-to-text recognition that may be utilized for several tasks at universities, such as passing out circulars, engaging with parents, and assisting them with navigation. The goal of this work is to create a robot that can communicate with humans using Spoken Natural Language. Two languages will be highlighted here: English and Kannada. This system employs a module that recognizes human speech, which is then processed and used to act or respond to the user's order. In this fashion, the voice-activated attender robot can be used in a university setting. However, it is not confined to universities. It can also be used in locations such as train stations, bus stops, large factories, and other similar settings.

scholarly journals Voice Recognition System Through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 4478-4483
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Voice Recognition ◽  
Recognition System ◽  
New Method ◽  
Human Voice ◽  
Developing Area ◽  
Voice Processing ◽  
The Voice

Human voice recognition by computers has been ever developing area since 1952. It is challenging task for a computer to understand and act according to human voice rather than to commands or programs. The reason is that no two human’s voice or style or pitch will be similar and every word is not pronounced by everyone in a similar fashion. Background noises and disturbances may confuse the system. The voice or accent of the same person may change according to the user’s mood, situation, time etc. despite of all these challenges, voice recognition and speech to text conversion has reached a successful stage. Voice processing technology deserves still more research. As a tip of iceberg of this research we contribute our work on this are and we propose a new method i.e., VRSML (Voice Recognition System through Machine Learning) mainly focuses on Speech to text conversion, then analyzing the text extracted from speech in the form of tokens through Machine Learning. After analyzing the derived text, reports are created in textual as well graphical format to represent the vocabulary levels used in that speech. As Supervised learning algorithm from Machine Learning is employed to classify the tokens derived from text, the reports will be more accurate and will be generated faster.

scholarly journals DGR: Deep Gender Recognition of Human Speech

2019 ◽  
Author(s):  
Rami S. Alkhawaldeh
Keyword(s):  
Feature Selection ◽  
Voice Recognition ◽  
Support Vector ◽  
Classification Models ◽  
Training Set ◽  
Human Voice ◽  
Voice Signal ◽  
Signal Features ◽  
Selection Algorithms ◽  
The Voice

The speech entailed in human voice comprises essentially para-linguistic information used in many voice-recognition applications. Gender voice-recognition is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e, male or female) from a voice signal. The contributions are involved in two folds: (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. Experimental results show the importance of sub-features over others, which are vital for enhancing the efficiency of classification models performance. Experimentation reveals that the best recall value is equal to 99.97%; 99.7% of two models of Deep Learning (DL) and Support Vector Machine (SVM) and with feature selection the best recall value is 100% for SVM techniques.

scholarly journals Virtual Voice Assistant

2021 ◽  
Vol 9 (VI) ◽  
pp. 4097-4101
Author(s):  
Ch. V. Tejaswi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Speech Synthesis ◽  
Voice Recognition ◽  
Computer Software ◽  
Human Beings ◽  
Desktop Application ◽  
Personal Assistant ◽  
The Voice

This is desktop application which can assist people with basic tasks using natural language. Virtual Voice Assistants can go online and search for an answer to a user’s question. Actions can be triggered using text or voice. Voice is the key. A virtual voice assistant is a personal assistant which uses natural language processing (NLP) , voice recognition and speech synthesis to provide a service through a particular application. Natural Language Processing in short is called as NLP. It is basically a branch of artificial intelligence which mainly deals with the interaction between personal computers and human beings using the natural language. The main objective of NLP is to read, convert, understand, and make use of the human languages in a manner that is valuable. Voice recognition is a hardware device or computer software program with the potential to decode the voice of human beings. Voice recognition is usually used to operate a gadget, execute commands, or write without making use of any mouse, keyboard, or press any buttons. Artificial production of human speech is called as Speech Synthesis. A system used for this purpose is called a speech computer or speech synthesizer and can be implemented in many products of software’s and hardware’s.

scholarly journals SPEECH RECOGNITION BASED EMBEDDED CONTROL SYSTEM

2014 ◽  
pp. 8-14
Author(s):  
PARVEZ HASAN ◽  
V. K. JOSEPH
Keyword(s):  
Control System ◽  
User Interface ◽  
Speech Recognition ◽  
Natural Language ◽  
Embedded System ◽  
Voice Recognition ◽  
Test Plan ◽  
Embedded Control ◽  
Novice User ◽  
Embedded Control System

The purpose of this project is to operate or control Embedded system based on voice recognition, which helps to introduce hearing as well as Natural Language (NL) interface through Speech for the Human-Embedded system interaction. One of the important goals of pursued project is to introduce suitable user interface for novice user and the test plan is to design accordingly.

scholarly journals A Novel automatic voice recognition system based on text-independent in a noisy environment

2020 ◽  
Vol 10 (4) ◽  
pp. 3643
Author(s):  
Motaz Hamza ◽  
Touraj Khodadadi ◽  
Sellappan Palaniappan
Keyword(s):  
Voice Recognition ◽  
Recognition System ◽  
Primary Objective ◽  
Training Data ◽  
Gaussian Mixtures ◽  
Noisy Environment ◽  
Human Voice ◽  
Voice Signal ◽  
Noise Filter ◽  
The Voice

Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user to speak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. The voice recognition was done in two steps: training and testing. During the training a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisy environment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noise methods were applied, we investigated different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that subsequently improved the performance of the automatic voice recognition system.

scholarly journals Smart information desk system with voice assistant for universities

2021 ◽  
Vol 11 (6) ◽  
pp. 5206
Author(s):  
Dabiah Alboaneen ◽  
Dalia Alsaffar ◽  
Amani Alqahtani ◽  
Lama Alamri ◽  
Amjad Alfahhad ◽  
...  
Keyword(s):  
Voice Recognition ◽  
General Information ◽  
Raspberry Pi ◽  
Mirror Surface ◽  
Academic Information ◽  
The University ◽  
The Voice

This article aims to develop a smart information desk system through a smart mirror for universities. It is a mirror with extra capabilities of displaying answers for academic inquiries such as asking about the lecturers’ office numbers and hours, exams dates and times on the mirror surface. In addition, the voice recognition feature was used to answer spoken inquiries in audio responds to serve all types of users including disabled ones. Furthermore, the system showed general information such as date, weather, time and the university map. The smart mirror was connected to an outdoor camera to monitor the traffics at the university entrance gate. The system was implemented on a Raspberry Pi 4 model B connected to a two-way mirror and an infrared (IR) touch frame. The results of this study helped to overcome the problem of the information desk absence in the university. Therefore, it helped users to save their time and effort in making requests for important academic information.

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

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