Abstract
Nowadays, 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.
Highlights
Speech is a longitudinal wave that propagated through a medium, which can be solid, liquid, or gaseous [1]
We determine the features suitable for the deep bidirectional long short-term memory (LSTM) and consider complete sentences consisting of subject, predicate, object, and adverb spoken by some respondents
Results with a word error rate (WER) of 2.0784% in training scenario B, testing with Mel frequency cepstral coefficients (MFCCs) yielded the best results with a WER of 0.2745% in training scenarios D and E. These results indicate that the MFCCs are more suitable than spectrograms for speech-to-text conversion with the deep bidirectional LSTM algorithm
Summary
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. Humans communicate 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]. 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]
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