Jawi Character Speech-to-Text Engine Using Linear Predictive and Neural Network for Effective Reading

Othman, Zaini Arifah; Razak, Zaidi; Abdullah, Nor Aniza; Yusoff, Zulkifli Mohd

doi:10.1109/ams.2009.94

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Ahmed et al utilized a hidden Markov model (HMM) for English and Arabic speech recognition [6]. Hotta [7] and Othman [8] performed speech-to-text conversion using neural networks in Japanese and Jawi, respectively. Kumar et al [9] used a recurrent neural network (RNN) for speech-to-text conversion in Hindi, and Laksono et al [10] used connectionist temporal classification (CTC), which is usually applied on top of an RNN, for speech-to-text conversion in Indonesian and Javanese.…”

Section: Introductionmentioning

confidence: 99%

Speech-to-Text Conversion in Indonesian Language Using a Deep Bidirectional Long Short-Term Memory Algorithm

Dwijayanti¹,

Tami²,

Suprapto³

2021

IJACSA

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Now-a-days, speech is used also for communication between humans and computers, which requires conversion from speech to text. Nevertheless, few studies have been performed on speech-to-text conversion in Indonesian language, and most studies on speech-to-text conversion were limited to the conversion of speech datasets with incomplete sentences. In this study, speech-to-text conversion of complete sentences in Indonesian language is performed using the deep bidirectional long short-term memory (LSTM) algorithm. Spectrograms and Mel frequency cepstral coefficients (MFCCs) were utilized as features of a total of 5000 speech data spoken by ten subjects (five males and five females). The results showed that the deep bidirectional LSTM algorithm successfully converted speech to text in Indonesian. The accuracy achieved by the MFCC features was higher than that achieved with the spectrograms; the MFCC obtained the best accuracy with a word error rate value of 0.2745% while the spectrograms were 2.0784%. Thus, MFCCs are more suitable than spectrograms as feature for speech-to-text conversion in Indonesian. The results of this study will help in the implementation of communication tools in Indonesian and other languages.

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Section: Introductionmentioning

confidence: 99%

Speech-to-Text Conversion in Indonesian Language Using a Deep Bidirectional Long Short-Term Memory Algorithm

Dwijayanti¹,

Tami²,

Suprapto³

2021

IJACSA

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“…Since most of Malaysian, especially Malay people used to use Romanised Malay to read and write, Jawi scripting is being neglected. Nevertheless, Jawi scripting should be preserved even it is a traditional script use in Malaysia [4]. There are digitized Jawi script contributed by the Malaysian and Indonesian developer, but the target user is more to the normal user.…”

Section: Introductionmentioning

confidence: 99%

jFakih: Modelling mobile learning game

Hussain

Mutalib

Yasin

2014

2014 International Conference on Computer and Information Sciences (ICCOINS)

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This study introduce a new method for learning Jawi for deaf children by adding some hand signs and named the method as jFakih. Since Fakih is a learning method for deaf children to read Quran, it's also possible to be used in Jawi learning for deaf children. Fakih used number and colours to recognize the Arabic character and also the script. We also designed the reference model for jFakih for future development of mobile application. By using waterfall-agile hybrid method, we gathered the requirements and designed the model using UML technique. The model was validated through prototype development and some survey was implemented to get feedbacks from users.

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