Sign Language to Speech Conversion

Vishal, Dasari; Aishwarya, H M; Nishkala, K; Royan, B Toshitha; Ramesh, T. K.

doi:10.1109/iccic.2017.8523832

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…People with disabilities can live independently thanks to smart sensors and communication technology. For persons with physical limitations, there are several automation devices available [3]. Accessibility, navigation, monitoring, and communication are some of the most significant applications.…”

Section: Communication Technologymentioning

confidence: 99%

Designing Assistive Devices to Support Learning for Students with Hearing Impairments

Ismail¹,

Yasin²,

Ismail³

et al. 2023

IJANSER

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Sign language recognition has become a crucial area of research, especially in the field of assistive technology for hearing- and speech-impaired individuals. This paper presents a new project aimed at developing a device that translates American Sign Language (ASL) into spoken language using the Internet of Things (IoT) and sensors. The system design covers 30 basic gestures and focuses on making communication easier for hearing-impaired students in technical programs. The project addresses the challenges and limitations of sign language recognition, and aims to reduce the communication gap between hearing-impaired and normal individuals. The implementation of this project is expected to result in a more accessible and inclusive society for all.

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Section: Communication Technologymentioning

confidence: 99%

Designing Assistive Devices to Support Learning for Students with Hearing Impairments

Ismail¹,

Yasin²,

Ismail³

et al. 2023

IJANSER

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“…There is a need for a comfortable and simplified life that paves the way for opportunities to enhance the existing technology. This is because certain kinds of data gloves still offer an inferior wearing experience that causes discomfort due to the rigid and thick characteristics of the sensors [43], [44].…”

Section: ) Design Enhancementmentioning

confidence: 99%

“…Notably, in cases where the flex sensors were positioned in such a way that posits the end of the flex sensor not being in the fold of the hand, would result in the sensor breaking, and thus affecting the recognition responses [61]. This matter specifies the biggest problem that is related to the sensors found on the market [62], [44], [63]. In addition to that, researchers pointed that the third aspect that is related to hardware issues is the need for computational power which posed a challenge if applied to a portable device.…”

Section: ) the Challenges Related To Hardwarementioning

confidence: 99%

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

et al. 2022

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Sign language is the predominant mode of communication for the Deaf community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor-based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

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“…A PIC microcontroller can be used with flex sensor and accelerometer along with an amplifier to boost the voltage inputs from a flex sensor, and then finally a speaker to produce sound [10]. The design of a wearable band has been discussed in [11]. The band houses EMG sensors which measure the biopotential in the muscles moving the hands.…”

Section: Introductionmentioning

confidence: 99%

Gesture To Speech Conversion using Flex sensors, MPU6050 and Python

Mehra*¹,

Choudhury²,

Choubey³

2019

IJEAT

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Communicating through hand gestures is one of the most common forms of non-verbal and visual communication adopted by speech impaired population all around the world. The problem existing at the moment is that most of the people are not able to comprehend hand gestures or convert them to the spoken language quick enough for the listener to understand. A large fraction of India’s population is speech impaired. In addition to this communication to sign language is not a very easy task. This problem demands a better solution which can assist speech impaired population conversation without any difficulties. As a result, reducing the communication gap for the speech impaired. This paper proposes an idea which will assist in removing or at least reducing this gap between the speech impaired and normal people. The research going on this area mostly focuses on image processing approaches. However, a cheaper and user-friendly approach has been used in this paper. The idea is to make a glove that can be worn by the speech impaired people which will further be used to convert the sign language into speech and text. Our prototype involves Arduino Uno as a microcontroller which is interfaced with flex sensors and accelerometer, gyroscopic sensor for reading the hand gestures. Furthermore, to perform better execution, we have incorporated an algorithm for better interpretation of data and therefore producing more accurate result. Thereafter, we use python to interface Arduino Uno with a microprocessor and finally converting into speech. The prototype has been calibrated in accordance with the ASL (American Sign Language).

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Sign Language to Speech Conversion

Cited by 6 publications

References 6 publications

Designing Assistive Devices to Support Learning for Students with Hearing Impairments

Designing Assistive Devices to Support Learning for Students with Hearing Impairments

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

Gesture To Speech Conversion using Flex sensors, MPU6050 and Python

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