scholarly journals Edge Container for Speech Recognition

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2420
Author(s):  
Lukáš Beňo ◽  
Rudolf Pribiš ◽  
Peter Drahoš
Keyword(s):  
Speech Recognition ◽  
Control Function ◽  
Independent Solution ◽  
Local Network ◽  
Raspberry Pi ◽  
Additional Contribution ◽  
Industrial Systems ◽  
Voice Control ◽  
Actual Speech ◽  
The Voice

Containerization has been mainly used in pure software solutions, but it is gradually finding its way into the industrial systems. This paper introduces the edge container with artificial intelligence for speech recognition, which performs the voice control function of the actuator as a part of the Human Machine Interface (HMI). This work proposes a procedure for creating voice-controlled applications with modern hardware and software resources. The created architecture integrates well-known digital technologies such as containerization, cloud, edge computing and a commercial voice processing tool. This methodology and architecture enable the actual speech recognition and the voice control on the edge device in the local network, rather than in the cloud, like the majority of recent solutions. The Linux containers are designed to run without any additional configuration and setup by the end user. A simple adaptation of voice commands via configuration file may be considered as an additional contribution of the work. The architecture was verified by experiments with running containers on different devices, such as PC, Tinker Board 2, Raspberry Pi 3 and 4. The proposed solution and the practical experiment show how a voice-controlled system can be created, easily managed and distributed to many devices around the world in a few seconds. All this can be achieved by simple downloading and running two types of ready-made containers without any complex installations. The result of this work is a proven stable (network-independent) solution with data protection and low latency.

scholarly journals IoT Based Voice Controlled Multitasking System for Home

2019 ◽  
Vol 9 (1) ◽  
pp. 4265-4268
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Raspberry Pi ◽  
Family Unit ◽  
Human Voice ◽  
The Family ◽  
Voice Control ◽  
The Voice ◽  
Dedicated Hardware

Internet of Things is a rising innovation that makes our world more astute. In recent years, there has been immense development in the realm of insightful gadgets for home mechanization. Such contraptions are planned so as to facilitate communication among individuals and everyday home obligations. This paper exhibits a voice-controlled smart home with multi-functions using ESP32 as the wireless choice. Voice control (using human voice to control any load like light, fan, ac, geyser, motor etc.). The voice-commands are recognized by a dedicated hardware module and the recognized data is sent to database using ESP32. On the accepting unit, raspberry pi peruses the information from the database and deciphers the directions verbally expressed by the client and controls the family unit apparatuses.

Design of Voice Control System for Smart Home Based on STM32

2015 ◽  
Vol 734 ◽  
pp. 369-374 ◽  
Author(s):  
Ping Qian ◽  
Ying Zhen Zhang ◽  
Yu Li
Keyword(s):  
Control System ◽  
Speech Recognition ◽  
Smart Home ◽  
High Performance ◽  
Practical Application ◽  
Ultra Low Power ◽  
Home Based ◽  
Voice Control ◽  
The Voice ◽  
The Internet Of Things

The application of embedded speech recognition technology in the smart home is researched, combining of the Internet of Things, the voice control system for smart home has been designed. The core processor chooses the high-performance Cortex-M4 MCU STM32F407VGT6 produced by STMicroelectronics. The system contains a hardware unit based on LD3320 for speaker-independent speech recognition. RF wireless communication uses ultra-low power chip CC1101 and GSM employ SIM900A. Real-time operating system FreeRTOS is used for multitask scheduling and the operation of household devices. The practical application verifies that this voice control system practicably can identify voice commands quickly and accurately, complete the control actions primely, has a wide application prospect.

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 Home Automation by Speech Recognition

2019 ◽  
Vol 8 (12S) ◽  
pp. 241-243
Keyword(s):  
Smart Home ◽  
Home Automation ◽  
Voice Command ◽  
Control Interface ◽  
Voice Control ◽  
Efficient Control ◽  
Speech Control ◽  
Actual Speech ◽  
Free Interaction ◽  
The Voice

The speech control is now most important feature of a smart home. In this paper, we projected voice command module that is used to enable the user for a hands-free interaction between smart home and himself. We presented mainly three components that is required for a simple and an efficient control of smart home device(s). The wake-up-word parts allows the actual speech command processing. The voice recognition part maps the spoken voice command to text and then Voice Control Interface passes that text into an appropriate JSON format for the home automation. We evaluate every possibility of using a voice control module in the smart home by distinctly analyzing each and every component of module

The Design and Realization of Remote Voice Control System Based on the Intelligent Home

2014 ◽  
Vol 530-531 ◽  
pp. 1112-1118
Author(s):  
Ye Fen Yang ◽  
Jun Zhang ◽  
Dong Hai Zeng
Keyword(s):  
Control System ◽  
Mobile Phone ◽  
Control Function ◽  
Voice Recognition ◽  
Security Requirements ◽  
Intelligent Home ◽  
Design Program ◽  
Wide Range ◽  
Voice Control ◽  
The Voice

A design program of remote voice control system is presented based on the intelligent home on Android mobile phone platform. Via the voice recognition of Android mobile phone, the intelligent home can have a remote voice control function by this program, which greatly improves the security requirements of the intelligent home. This system is tested and proved its real-time, effectiveness and stability. Meanwhile, it can also provide a practical reference solution for human-computer interaction, having a wide range of application.

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.

scholarly journals Realization of speech in design intent of geometric modelling

2017 ◽  
Vol 7 (1.3) ◽  
pp. 121
Author(s):  
Sreeja B P ◽  
Amrutha K G ◽  
Jeni Benedicta J ◽  
Kalaiselvi V ◽  
Ranjani R
Keyword(s):  
Speech Recognition ◽  
Geometric Modeling ◽  
Geometric Modelling ◽  
Design Intent ◽  
Voice Input ◽  
Interactive Mode ◽  
Modeling Software ◽  
Speech Recognition Engine ◽  
The Voice ◽  
Geometric Designs

The conventional interactive mode is especially used for geometric modeling software. This paper describes, a voice-assisted geometric modeling mechanism to improve the performance of modeling, speech recognition technology is used to design this model. This model states that after receiving the voice command, the system uses the speech recognition engine to identify the voice commands, then the voice commands identified are parsed and processed to generate the geometric design based on the users voice input dimensions, The outcome of the system is capable of generating the geometric designs to the user via speech recognition. This work also focuses on receiving the feedback from the users and customized the model based on the feedback.

A Design of a Speech Conversion System from PuTongHua to Cantonese Based on iFLY MSP 2.0

2013 ◽  
Vol 373-375 ◽  
pp. 504-508
Author(s):  
Rong Gui Ma ◽  
Fang Zhou Liu
Keyword(s):  
Speech Recognition ◽  
Learning System ◽  
Computer Assisted ◽  
Text To Speech ◽  
Computer Assisted Learning ◽  
Key Technology ◽  
Conversion System ◽  
Recognition Function ◽  
Text Information ◽  
The Voice

The paper analyzes the working theory of a Speech Conversion System from PuTongHua to Cantonese based on iFLY MSP 2.0. In the system, QISR interface is chosen to complete speech recognition function which is the key technology to convert the voice information into the corresponding text information. Moreover, the QTTS interface is chosen to complete the text to speech function which is the key technology to transform the text which is the result of the speech recognition into the spoken information in Cantonese and then output. Finally, the computer assisted learning system is designed successfully in the environment of Visual C++ 6.0.

scholarly journals Android Based Voice Activated Wheel Chair for Disabled

2020 ◽  
Vol 9 (3) ◽  
pp. 3508-3509
Keyword(s):  
Speech Recognition ◽  
Smart Phone ◽  
Dc Motor ◽  
Older Individuals ◽  
Recognition Model ◽  
Human Voice ◽  
Dc Motors ◽  
Wheel Chair ◽  
The Voice

The aim of the project is to develop a wheel chair which can be controlled by voice of the person. It is based on the speech recognition model. The project is focused on controlling the wheel chair by human voice. The system is intended to control a wheel seat by utilizing the voice of individual. The structure of this framework will be particularly valuable to the crippled individual and furthermore to the older individuals. It is a booming technology which interfaces human with machine. Smart phone device is the interface. This will allow the challenging people to move freely without the assistant of others. They will get a moral support to live independently .The hardware used are Arduino kit, Microcontroller, Wheelchair and DC motors. DC motor helps for the movement of wheel chair. Ultra Sonic Sensor senses the obstacles between wheelchair and its way.

scholarly journals Voice Controlled Vehicle Dashboard

2020 ◽  
Vol 9 (1) ◽  
pp. 1022-1027
Keyword(s):  
Speech Recognition ◽  
Speaker Recognition ◽  
Feature Matching ◽  
Heavy Traffic ◽  
Recognition Rate ◽  
Recognition System ◽  
Low Noise ◽  
Raspberry Pi ◽  
Mel Frequency Cepstral Coefficients ◽  
Power Spectral

Driving a vehicle or a car has become tedious job nowadays due to heavy traffic so focus on driving is utmost important. This makes a scope for automation in Automobiles in minimizing human intervention in controlling the dashboard functions such as Headlamps, Indicators, Power window, Wiper System, and to make it possible this is a small effort from this paper to make driving distraction free using Voice controlled dashboard. and system proposed in this paper works on speech commands from the user (Driver or Passenger). As Speech Recognition system acts Human machine Interface (HMI) in this system hence this system makes use of Speaker recognition and Speech recognition for recognizing the command and recognize whether the command is coming from authenticated user(Driver or Passenger). System performs Feature Extraction and extracts speech features such Mel Frequency Cepstral Coefficients(MFCC),Power Spectral Density(PSD),Pitch, Spectrogram. Then further for Feature matching system uses Vector Quantization Linde Buzo Gray(VQLBG) algorithm. This algorithm makes use of Euclidean distance for calculating the distance between test feature and codebook feature. Then based on speech command recognized controller (Raspberry Pi-3b) activates the device driver for motor, Solenoid valve depending on function. This system is mainly aimed to work in low noise environment as most speech recognition systems suffer when noise is introduced. When it comes to speech recognition acoustics of the room matters a lot as recognition rate differs depending on acoustics. when several testing and simulation trials were taken for testing, system has speech recognition rate of 76.13%. This system encourages Automation of vehicle dashboard and hence making driving Distraction Free.

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