scholarly journals PERANCANGAN SMART DOOR LOCK MENGGUNAKAN VOICE RECOGNITION BERBASIS RAPBERRY PI 3

2018 ◽  
Vol 4 (2) ◽  
pp. 180-189
Author(s):  
Diah Aryani ◽  
Dedy Iskandar ◽  
Fitri Indriyani
Keyword(s):  
Speech Recognition ◽  
Computer System ◽  
Voice Recognition ◽  
Raspberry Pi ◽  
Servo Motor ◽  
Left Behind ◽  
Door Lock

The server door is the main access to enter the server room. Currently the door lock on the server room is still done manually using the physical key as a tool to open or lock the door. Physical keys are easily lost or left behind which results in the officer not being able to enter the server room. This resulted in the officer can not access the server. Based on these reasons, the server door is integrated with a computer system that can unlock the door using voice recognition to unlock the server door. Voice recognition is able to identify a person through his voice. Voice recognition is divided into 2 parts namely speech recognition and speeker recognition. Meanwhile, the authors use the speech recognition section to open thedoor server door lock. Where, speech recognition can identify what is spoken by someone. The design of this tool is made using Raspberry pi 3 as the processing center and ULN2803 as ic to increase the voltage so that it can move the solenoid that serves to move the doorlock. Then raspberry gives command to the servo motor to open the door. Only staff who have id and password are only able to open the door lock on the server room using voice recognition. While those who do not have id and password can not unlock the door in the server room. So with the design of smart door lock tool using voice recognition raspberry-based pi 3 provides a level of security and access more computerized.

scholarly journals Rancang Bangun Sistem Kendali Berbasis Googlespeech Untuk Aktivasi Peralatan Listrik Rumah

2018 ◽  
Vol 5 (2) ◽  
pp. 83-98
Author(s):  
Tesar Kurniawan ◽  
Nursin Nursin ◽  
Muhamad Amin Bakrie ◽  
Seta Samsiana
Keyword(s):  
Speech Recognition ◽  
Free Space ◽  
Recognition Accuracy ◽  
Voice Recognition ◽  
Smart Phones ◽  
Maximum Distance ◽  
Software Developer ◽  
Door Lock ◽  
App Inventor ◽  
Bahasa Indonesia

App inventor adalah media pengembang perangkat lunak untuk sistem android, yang memudahkan para  pengembangnya mengembangkan  idenya,  salah satunya aplikasi yang mampu mengendalikan peralatan listrik rumah menggunakan suara melalui  telepon  pintar  yang dapat mengontrol aktivasi peralatan listrik rumah. Google  Speech  digunakan untuk pengenalan suara yang  kemudian memberikan input ke Arduino untuk mengendalikan aktivasi peralatan listrik rumah, Peralatan listrik rumah seperti lampu, motor pompa akuarium, kipas, door lock dan motor servo yang memanfaatkan relay sebagai driver, kemudian dilakukanlah pengujian dan penelitian pada laporan ini berisi tentang pengujian akurasi pengenalan suara google  Speech dan pengujian jarak koneksi Bluetooth. Tingkat keakurasian pada google  Speech  yang paling baik dari 3 bahasa yaitu Bahasa Indonesia disusulBahasa jawa  dan terakhir Bahasa sunda, sedangkan untuk jarak koneksi pada Bluetooth dapat dioperasikan jarak maksimal pada ruang bebas adalah 20 m dan jarak maksimal pada ruang berhalangan adalah 13 m. App inventor is a software developer media for android systems, which makes it easy for developers to develop their ideas, i.e an application that is able to control home electrical appliances using voice over smart phones that can control the activation of home electrical appliances. Google Speech is used for voice recognition which then provides input to Arduino to control the activation of home electrical appliances, such as lamps, aquarium pump motors, fans, door locks. A servo motors is used as drivers, then test and research on this report Contains about Speech google speech recognition accuracy testing and Bluetooth connection distance testing. Level of accuracy on google Speech the best of 3 languages ie Indonesian followed by Java and last language Sundanese, while for the distance on the Bluetooth connection can be operated the maximum distance in free space is 20 m and the maximum distance in the absence room is 13 m.

scholarly journals Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction

2021 ◽  
pp. 095440542110144
Author(s):  
B Birch ◽  
CA Griffiths ◽  
A Morgan
Keyword(s):  
Speech Recognition ◽  
Voice Recognition ◽  
Human Robot Interaction ◽  
Hole Drilling ◽  
Time Warping ◽  
Mel Frequency Cepstral Coefficients ◽  
Robot Interaction ◽  
Extraction Algorithm ◽  
Dynamic Time ◽  
Manufacturing Environments

Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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 PEMBERI MAKAN OTOMATIS PADA KUCING MENGGUNAKAN RASPBERRY PI BERBASIS ANDROID

2020 ◽  
Vol 3 (2) ◽  
pp. 298-308
Author(s):  
Uci Rahmalisa ◽  
Mardeni Mardeni ◽  
Rialtra Helmi ◽  
Arie Linarta
Keyword(s):  
Programming Language ◽  
Raspberry Pi ◽  
Feeding Schedule ◽  
Servo Motor ◽  
Food Container ◽  
Python Language ◽  
Food Stock ◽  
And Performance ◽  
Automatic Scheduling ◽  
Predetermined Time

Keep a pet at home takes time and effort. For people who have very dense flurry of activity certainly keep a pet such as a cat would be very hard to do. A Raspberry Pi microcontroller is designed for the purpose of automatic feeding so it is easy to use. The workings of the tool are automatic scheduling using an Android-based smartphone so that the servo motor will open and close so that the cat food is taken out into the food container that has been provided. By using an Android-based smartphone, the feeding schedule can be set by the hour for each funnel. Equipped with a buzzer as a reminder of cat owners if the available food stock is low and must be immediately refilled. The programming language used is Python language. Based on testing and performance of "Automatic Cat Feeding Using Raspberry Pi Android Based" has shown results in accordance with the design that is able to open and close the funnel that fills the cat food container with a servo motor automatically by setting a predetermined time.

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.

Comparison of Voice-Automated Transcription and Human Transcription in Generating Pathology Reports

2003 ◽  
Vol 127 (6) ◽  
pp. 721-725
Author(s):  
Maamoun M. Al-Aynati ◽  
Katherine A. Chorneyko
Keyword(s):  
Speech Recognition ◽  
Voice Recognition ◽  
Computer Software ◽  
Continuous Speech ◽  
Continuous Speech Recognition ◽  
Accuracy Rate ◽  
Written Text ◽  
Computer Based ◽  
Pathology Reports ◽  
Recognition Software

Abstract Context.—Software that can convert spoken words into written text has been available since the early 1980s. Early continuous speech systems were developed in 1994, with the latest commercially available editions having a claimed accuracy of up to 98% of speech recognition at natural speech rates. Objectives.—To evaluate the efficacy of one commercially available voice-recognition software system with pathology vocabulary in generating pathology reports and to compare this with human transcription. To draw cost analysis conclusions regarding human versus computer-based transcription. Design.—Two hundred six routine pathology reports from the surgical pathology material handled at St Joseph's Healthcare, Hamilton, Ontario, were generated simultaneously using computer-based transcription and human transcription. The following hardware and software were used: a desktop 450-MHz Intel Pentium III processor with 192 MB of RAM, a speech-quality sound card (Sound Blaster), noise-canceling headset microphone, and IBM ViaVoice Pro version 8 with pathology vocabulary support (Voice Automated, Huntington Beach, Calif). The cost of the hardware and software used was approximately Can $2250. Results.—A total of 23 458 words were transcribed using both methods with a mean of 114 words per report. The mean accuracy rate was 93.6% (range, 87.4%–96%) using the computer software, compared to a mean accuracy of 99.6% (range, 99.4%–99.8%) for human transcription (P < .001). Time needed to edit documents by the primary evaluator (M.A.) using the computer was on average twice that needed for editing the documents produced by human transcriptionists (range, 1.4–3.5 times). The extra time needed to edit documents was 67 minutes per week (13 minutes per day). Conclusions.—Computer-based continuous speech-recognition systems in pathology can be successfully used in pathology practice even during the handling of gross pathology specimens. The relatively low accuracy rate of this voice-recognition software with resultant increased editing burden on pathologists may not encourage its application on a wide scale in pathology departments with sufficient human transcription services, despite significant potential financial savings. However, computer-based transcription represents an attractive and relatively inexpensive alternative to human transcription in departments where there is a shortage of transcription services, and will no doubt become more commonly used in pathology departments in the future.

Intelligent Door Lock System Based on Raspberry Pi

2021 ◽  
Author(s):  
Xiaomin Zhang ◽  
Mingyu Song ◽  
Yaohui Xu ◽  
Zhengwu Dai ◽  
Weicong Zhang
Keyword(s):  
Raspberry Pi ◽  
Door Lock

Voice/Speech Recognition Software

2013 ◽  
pp. 1005-1011
Author(s):  
Andrew Kitchenham ◽  
Doug Bowes
Keyword(s):  
Speech Recognition ◽  
Assistive Technology ◽  
Special Needs ◽  
Learning Process ◽  
Voice Recognition ◽  
Special Needs Students ◽  
Speech Recognition Software ◽  
The Disabled ◽  
Recognition Software

In this chapter, the authors discuss the promise of speech or voice recognition software and provide practical suggestions for the teacher or any stakeholder working with a disabled child. The authors begin the chapter with a brief overview of the legislation mandating the accommodation of special needs students in the classroom and discuss the implications of assistive technology. The authors then move on to an examination of the promise of the software. The authors end the chapter with practical ideas for implementation should the caregiver believe that voice recognition software will assist the disabled child in the learning process.

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