Speech recognition and Filipino sign language E-tutor system

2019 ◽  
Author(s):  
Mary Jane C. Samonte
Keyword(s):  
Speech Recognition ◽  
Sign Language ◽  
Tutor System

scholarly journals A new technology on translating Indonesian spoken language into Indonesian sign language system

2021 ◽  
Vol 11 (4) ◽  
pp. 3338
Author(s):  
Risky Aswi Ramadhani ◽  
I Ketut Gede Dharma Putra ◽  
Made Sudarma ◽  
Ida Ayu Dwi Giriantari
Keyword(s):  
Speech Recognition ◽  
Success Rate ◽  
Sign Language ◽  
Text Processing ◽  
New Technology ◽  
Confusion Matrix ◽  
Spoken Language ◽  
Language System ◽  
The People

People with hearing disabilities are those who are unable to hear, resulted in their disability to communicate using spoken language. The solution offered in this research is by creating a one way translation technology to interpret spoken language to Indonesian sign language system (SIBI). The mechanism applied here is by catching the sentences (audio) spoken by common society to be converted to texts, by using speech recognition. The texts are then processed in text processing to select the input texts. The next stage is stemming the texts into prefixes, basic words, and suffixes. Each words are then being indexed and matched to SIBI. Afterwards, the system will arrange the words into SIBI sentences based on the original sentences, so that the people with hearing disabilities can get the information contained within the spoken language. This technology success rate were tested using Confusion Matrix, which resulted in precision value of 76%, accuracy value of 78%, and recall value of 79%. This technology has been tested in SMP-LB Karya Mulya on the 7th grader students with the total of 9 students. From the test, it is obtained that 86% of students stated that this technology runs very well.

scholarly journals A Novel Technique for Speech Recognition and Visualization Based Mobile Application to Support Two-Way Communication between Deaf-Mute and Normal Peoples

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Kanwal Yousaf ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
Amjad Rehman ◽  
Muhammad Rashid ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Sign Language ◽  
Mobile Technology ◽  
Mobile Application ◽  
Technology Development ◽  
Normal Person ◽  
Mel Frequency Cepstral Coefficients ◽  
Face To Face ◽  
Analysis Of Results ◽  
Mobile Market

Mobile technology is very fast growing and incredible, yet there are not much technology development and improvement for Deaf-mute peoples. Existing mobile applications use sign language as the only option for communication with them. Before our article, no such application (app) that uses the disrupted speech of Deaf-mutes for the purpose of social connectivity exists in the mobile market. The proposed application, named as vocalizer to mute (V2M), uses automatic speech recognition (ASR) methodology to recognize the speech of Deaf-mute and convert it into a recognizable form of speech for a normal person. In this work mel frequency cepstral coefficients (MFCC) based features are extracted for each training and testing sample of Deaf-mute speech. The hidden Markov model toolkit (HTK) is used for the process of speech recognition. The application is also integrated with a 3D avatar for providing visualization support. The avatar is responsible for performing the sign language on behalf of a person with no awareness of Deaf-mute culture. The prototype application was piloted in social welfare institute for Deaf-mute children. Participants were 15 children aged between 7 and 13 years. The experimental results show the accuracy of the proposed application as 97.9%. The quantitative and qualitative analysis of results also revealed that face-to-face socialization of Deaf-mute is improved by the intervention of mobile technology. The participants also suggested that the proposed mobile application can act as a voice for them and they can socialize with friends and family by using this app.

scholarly journals Analisis dan Perancangan Kamus Interaktif Bahasa Isyarat Indonesia dengan Speech Recognition

2019 ◽  
Vol 1 (2) ◽  
pp. 110-115
Author(s):  
Ahmad Zuli Amrullah ◽  
Khurniawan Eko Saputro
Keyword(s):  
Speech Recognition ◽  
Sign Language ◽  
Hearing Impairment ◽  
Ordinary People ◽  
Application Development ◽  
Persons With Disabilities ◽  
Software Product ◽  
Economic Survey ◽  
System Errors ◽  
Or Applications

ABSTRAK     Intisari – Menurut data Survei Sosial Ekonomi Nasional (Susenas) pada tahun 2012 terdapat sekitar 9,9 juta anak Indonesia menyandang disabilitas. Sekitar 7.87% dari total jumlah penyandang disabilitas tersebut mengalami tunarungu atau keterbatasan mendengar. Penyandang tunarungu melakukan komunikasi dengan menggunakan Bahasa isyarat. Karena tidak semua orang mengerti dengan bahasa isyarat maka dibutuhkan alat bantu atau aplikasi untuk berkomunikasi dengan penyandang tunarungu. Keterbatasan dalam berkomunikasi antara orang biasa dengan penyandang tunarungu. Oleh karena ity, untuk membantu mahasiswa dan dosen berkomunikasi dengan mahasiswa yang tunarung maka dibutuhkan aplikasi kamus Bahasa isyarat dengan Speech Recognition. Pengembangan aplikasi ini menggunakan metode pengembangan aplikasi waterfall. Dimana setiap alur berjalan secara selaras dan memudahkan untuk mencari kesalahan system. Pengujian dilakukan dengan verifikasi kebutuhan untuk memastikan produk perangkat lunak yang dihasilkan sesuai dengan spesifikasi yang ditentukan. Kata Kunci: Bahasa isyarat; kamus; speech recognition; ABSTRACT   Digest - According to data from the National Socio-Economic Survey (Susenas) in 2012 there were around 9.9 million Indonesian children with disabilities. Around 7.87% of the total number of persons with disabilities experience hearing impairment or hearing impairment. People with hearing impairment communicate using sign language. Because not everyone understands sign language, tools or applications are needed to communicate with deaf people. Limitations in communicating between ordinary people and hearing impaired people. Therefore, to help students and lecturers communicate with students who are fussy, it requires a sign language dictionary application with Speech Recognition. This application development uses the waterfall application development method. Where each flow runs in harmony and makes it easy to find system errors. The test is carried out by verifying the need to ensure that the software product is produced according to the specified specifications.   Keywords: Signal language; dictionary; speech recognition;

scholarly journals Speech recognition techniques for a sign language recognition system

2007 ◽  
Author(s):  
Philippe Dreuw ◽  
David Rybach ◽  
Thomas Deselaers ◽  
Morteza Zahedi ◽  
Hermann Ney
Keyword(s):  
Speech Recognition ◽  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

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