Speech to Text of Patient Complaints for Bahasa Indonesia

Laksono, Teguh Puji; Hidayatullah, Ahmad Fathan; Ratnasari, Chanifah Indah

doi:10.1109/ialp.2018.8629161

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…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. Abidin et al presented an approach to obtain Indonesian voice-to-text data set using Time Delay Neural Network Factorization (TDNNF) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Most studies on speech-to-text conversion were limited to the conversion of words or incomplete sentences from a dataset, and very few studies considered speech-to-text conversion in Indonesian. Laksono et al [10] used DNN and CTC with MFCCs as the features for speech-to-text conversion in Indonesian and Javanese with a small number of Indonesian and Javanese words. However, the result showed low accuracy for both Indonesian and Javanese; thus, they might not be suitable for speech-to-text conversion.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%