scholarly journals Review of Voice Controlled Robotic Arm-Raspberry Pi

2021 ◽  
Vol 5 (2) ◽  
pp. 1-4
Author(s):  
B. Shoban Babu ◽  
V. Priyadharshini ◽  
Prince Patel
Keyword(s):  
Wireless Communication ◽  
Life Skills ◽  
Voice Recognition ◽  
Smart Phone ◽  
Assistive Robotics ◽  
Robotic Arm ◽  
Raspberry Pi ◽  
Automotive Applications ◽  
The Voice ◽  
Knowledge Communication

One of the most essential life skills is to be able to communicate easily. In order to produce greater comprehension, communication is described as transmitting knowledge. Communication and technologies are not mutually exclusive. Speech Recognition is a technique that facilitates the processing of voice information to text and is independent of the speaker. This enables it to be used in various applications, from digital assistants to machinery control. The aim of this paper is to study numerous robotic vehicles powered by human speech commands. To accomplish this functionality, most of these systems run with the use of an android smart phone that transmits voice commands to a raspberry pi. The voice-operated robot is used to build one moving object. It is moved as per the voice recognition module commands, and the robot obtains that command. The robot compares the command with the stored software and then sets the command using wireless communication as per voice. These suggested methods would be useful for devices such as assistive robotics for people with disabilities or automotive applications such as work robots.

scholarly journals Extending Smart Phone Based Techniques to Provide AI Flavored Interaction with DIY Robots, over Wi-Fi and LoRa interfaces

2019 ◽  
Vol 9 (3) ◽  
pp. 224 ◽  
Author(s):  
Dimitrios Loukatos ◽  
Konstantinos G. Arvanitis
Keyword(s):  
Credit Card ◽  
Learning Experience ◽  
Low Cost ◽  
Voice Recognition ◽  
Smart Phone ◽  
Learning Activity ◽  
Raspberry Pi ◽  
Programming Environments ◽  
Robotic Vehicle ◽  
Educational Applications

Inspired by the mobile phone market boost, several low cost credit card-sized computers have made the scene, able to support educational applications with artificial intelligence features, intended for students of various levels. This paper describes the learning experience and highlights the technologies used to improve the function of DIY robots. The paper also reports on the students’ perceptions of this experience. The students participating in this problem based learning activity, despite having a weak programming background and a confined time schedule, tried to find efficient ways to improve the DIY robotic vehicle construction and better interact with it. Scenario cases under investigation, mainly via smart phones or tablets, involved from touch button to gesture and voice recognition methods exploiting modern AI techniques. The robotic platform used generic hardware, namely arduino and raspberry pi units, and incorporated basic automatic control functionality. Several programming environments, from MIT app inventor to C and python, were used. Apart from cloud based methods to tackle the voice recognition issues, locally running software alternatives were assessed to provide better autonomy. Typically, scenarios were performed through Wi-Fi interfaces, while the whole functionality was extended by using LoRa interfaces, to improve the robot’s controlling distance. Through experimentation, students were able to apply cutting edge technologies, to construct, integrate, evaluate and improve interaction with custom robotic vehicle solutions. The whole activity involved technologies similar to the ones making the scene in the modern agriculture era that students need to be familiar with, as future professionals.

scholarly journals Voice Controlled Home Automation

2019 ◽  
pp. 235-240
Author(s):  
P. Kiran Kumar ◽  
G. Joga Rao ◽  
A. Suguna ◽  
K. Bhanu Prakash Kavya G ◽  
P. Venkata Srikar Srihari
Keyword(s):  
Power Supply ◽  
Voice Recognition ◽  
Smart Phone ◽  
Home Automation ◽  
Disabled People ◽  
Voice Input ◽  
Voice Command ◽  
Home Appliances ◽  
The Voice ◽  
At Home

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

scholarly journals Comparison of CNNs and SVM for voice control wheelchair

2020 ◽  
Vol 9 (3) ◽  
pp. 387
Author(s):  
Mohammad Shahrul Izham Sharifuddin ◽  
Sharifalillah Nordin ◽  
Azliza Mohd Ali
Keyword(s):  
Feature Extraction ◽  
Processing Time ◽  
Voice Recognition ◽  
The Other ◽  
Raspberry Pi ◽  
Intelligent Wheelchair ◽  
Voice Control ◽  
Voice Data ◽  
The Voice ◽  
Feature Extraction Technique

In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+  to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.

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.

Voice Assistant — Application of Speech Recognition Technology in the Android System

2014 ◽  
Vol 596 ◽  
pp. 384-387
Author(s):  
Ge Liu ◽  
Hai Bing Zhang
Keyword(s):  
Speech Recognition ◽  
Service Providers ◽  
Voice Recognition ◽  
Working Process ◽  
Bottleneck Problems ◽  
Speech Recognition Technology ◽  
Android System ◽  
The Voice

This paper introduces the concept of Voice Assistant, the voice recognition service providers, several typical Voice Assistant product, and then the basic working process of the Voice Assistant is described in detail and proposed the technical bottleneck problems in the development of Voice Assistant software.

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