Sign Language to Speech Converter Using Raspberry-Pi

Silent speakers face a lot of problems when it comes to communicate their thoughts and views. Furthermore, only few people know the sign language of these silent speakers. They tend to feel awkward to take part any exercises with the typical individuals. They require gesture based communication mediators for their interchanges. The solution to this problem is to provide them a better way to take their message across, “Smart Finger Gesture Recognition System for Silent Speakers” which has been proposed. Instead of using sign language, gesture recognition is done with the help of finger movements. The system consists of data glove, flex sensors, raspberry pi. The flex sensors are fitted on the data gloves and it is used to recognize the finger gestures. Then the ADC module is used to convert the analog values into digital form. After signal conversion, the value is given to Raspberry Pi 3, and it converts the signals into audio output as well as text format using software tool. The proposed framework limits correspondence boundary between moronic individuals and ordinary individuals. Therefore, the recognized finger gestures are conveyed into speech and text so that the normal people can easily communicate with dumb people.

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SIGN LANGUAGE TO VOICE RECOGNITION SYSTEM USING RASPBERRY Pi

i-manager s Journal on Digital Signal Processing ◽

10.26634/jdp.8.2.18181 ◽

2020 ◽

Vol 8 (2) ◽

pp. 14

Author(s):

J. MANIKANDAN ◽

M. THANKAM ◽

K. P. AISHWARYA ◽

S. RADHA ◽

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...

Keyword(s):

Sign Language ◽

Voice Recognition ◽

Recognition System ◽

Raspberry Pi

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Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures

Electronics ◽

10.3390/electronics10020182 ◽

2021 ◽

Vol 10 (2) ◽

pp. 182

Author(s):

Aveen Dayal ◽

Naveen Paluru ◽

Linga Reddy Cenkeramaddi ◽

Soumya J. ◽

Phaneendra K. Yalavarthy

Keyword(s):

Deep Learning ◽

Sign Language ◽

Computing System ◽

Learning Model ◽

Edge Computing ◽

Raspberry Pi ◽

Authentication Code ◽

Hand Gestures ◽

Design And Implementation ◽

Deep Learning Model

Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using ‘authentication code’. The ‘authentication code’ is an ‘n’ digit numeric code and the digits are hand gestures of sign language digits. We propose a memory-efficient deep learning model to classify the hand gestures of the sign language digits. The proposed deep learning model is based on the bottleneck module which is inspired by the deep residual networks. The model achieves classification accuracy of 99.1% on the publicly available sign language digits dataset. The model is deployed on a Raspberry pi 4 Model B edge computing system to serve as an edge device for user verification. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and stores it in the buffer. In the second step, the model classifies the digit with the inference rate of 280 ms, by taking the first image in the buffer as input.

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Hand Gesture to Speech and Text Conversion Device

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2260.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1241-1246

Keyword(s):

Sign Language ◽

Angular Displacement ◽

Bending Moment ◽

Electronic System ◽

Raspberry Pi ◽

Normal Person ◽

Accelerometer Sensor ◽

Emergency Situations ◽

Normal Humans ◽

The Subject

Sign language and facial expressions are the major means of communication for the speech flawed people. General people can understand the facial expression to an extent but cannot understand the sign language. Dumb people are unable to express their thoughts to normal humans. To reduce this gap of communication, this paper presents an electronic system which will help the mute people to exchange their ideas with the normal person in emergency situations. The system consists of a glove that can be worn by the subject which will convert the hand gestures to speech and text. The message displayed will also help deaf people to understand their thoughts. This prototype involves raspberry pi 3 as the micro-controller along with the flex sensors, accelerometer sensor. The resistance of the flex sensor changes due to the bending moment of the fingers of the subject. The accelerometer measures the angular displacement of the wrist along the y-axis. The microcontroller takes the input from the two sensors and matches it with the pre-programmed values and plays the respective message. The system makes use of python and its libraries for microcontroller programming.

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Glove Translates Sign Language Into Spoken Words

Leader Mag Digital Object Group ◽

10.1044/leader.nib3.25102020.15 ◽

2020 ◽

Keyword(s):

Sign Language ◽

Spoken Words

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Understanding the Communication Needs of Culturally and Linguistically Diverse Students Who Are Deafblind

Perspectives of the ASHA Special Interest Groups ◽

10.1044/persp2.sig12.81 ◽

2017 ◽

Vol 2 (12) ◽

pp. 81-88

Author(s):

Sandy K. Bowen ◽

Silvia M. Correa-Torres

Keyword(s):

Sign Language ◽

Augmentative And Alternative Communication ◽

Linguistically Diverse ◽

Spoken Language ◽

Culturally And Linguistically Diverse ◽

Alternative Communication ◽

Linguistically Diverse Students ◽

The Past ◽

Communication Needs ◽

Different Cultures

America's population is more diverse than ever before. The prevalence of students who are culturally and/or linguistically diverse (CLD) has been steadily increasing over the past decade. The changes in America's demographics require teachers who provide services to students with deafblindness to have an increased awareness of different cultures and diversity in today's classrooms, particularly regarding communication choices. Children who are deafblind may use spoken language with appropriate amplification, sign language or modified sign language, and/or some form of augmentative and alternative communication (AAC).

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