(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 3, 2021 Speech-to-Text Conversion in Indonesian Language Using a Deep Bidirectional Long Short-Term Memory Algorithm Suci Dwijayanti 1 , Muhammad Abid Tami 2 , Bhakti Yudho Suprapto 3 Department of Electrical Engineering, Universitas Sriwijaya, Indralaya, Indonesia Abstract—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. Keywords—Speech-to-text; Deep Bidirectional Long Short- Term Memory (LSTM); spectrogram; Mel frequency cepstral coefficients (MFCC); word error rate I. INTRODUCTION Speech is a longitudinal wave that propagated through a medium, which can be solid, liquid, or gaseous [1]. Humans utilize speech as a primary component of communication to exchange information. Today, humans communicate also with computers; generally, this communication requires the conversion of speech into text [2]. This process involves various stages of conversion and outputs data consisting of numbers that can be processed by a computer into text [3]. Speech-to-text conversion can be implemented in various applications, such as communication tools for deaf people [2], smart homes [4], and translators [5]. Some studies have investigated speech-to-text conversion in various languages. 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. Abidin et al. presented an approach to obtain Indonesian voice-to-text data set using Time Delay Neural Network Factorization (TDNNF) [11]. Mon and Tun [12] proposed the HMM method, which uses Mel frequency cepstral coefficients (MFCCs) as features. Because they used a large dataset of English words, the HMM was ineffective owing to the high probability of similarity between words. Zhang [13] used a combination of the deep neural network (DNN) and HMM model for English speech recognition and showed that DNN-HMM was superior to the traditional Gaussian mixture model (GMM)-HMM method. Nevertheless, it still had low accuracy. Liu et al. [14] had shown that the RNN together with Long Short Term Memory (LSTM) improved the performance of speech recognition on the ChiME-5 dataset. Meanwhile, Wu et al. [15] and He [16] utilized RNN-LSTM for Chinese dataset, and the accuracy of speech recognition was improved. 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. In this study, we perform speech-to-text conversion in Indonesian using a deep bidirectional long short-term memory (LSTM) algorithm. We determine the features suitable for the deep bidirectional LSTM and consider complete sentences consisting of subject, predicate, object, and adverb spoken by some respondents. The rest of this paper is organized as follows. In Section 2, the research method used in this study is presented. Section 3 reports and discusses the results. Finally, the paper is concluded in Section 4. II. MATERIALS AND METHODS A. Data Collection The speech data were obtained from ten speakers (five males and five females). Every speaker uttered ten sentences in Indonesian consisting of a subject, predicate, object, and adverb, as presented in Table I. Each sentence was uttered 50 times; thus, a total of 5000 sentences were recorded. Data 225 | Page www.ijacsa.thesai.org