Implementation of the LSTM Model for Speech-to-Text Systems in the Recognition of the Walikan Language of Malang

Author's Country: Indonesia

Abstract

This study developed a Speech-to-Text (STT) system based on the Long Short-Term Memory (LSTM) model to recognize and convert speech in the Malang Walikan language into text. The Malang Walikan language has a unique linguistic structure in the form of word reversal, which poses a challenge in speech recognition. The data used consisted of 1,000 sentences collected from social media and direct recordings. The data was processed using Mel Frequency Cepstral Coefficients (MFCC) and then used to train the LSTM model.The system's performance was evaluated using the Word Error Rate (WER), Character Error Rate (CER), and Average Test Loss metrics. The best results obtained showed a WER value of 1.0 on a 699:300 data split, a CER of 0.78 on a 799:200 split, and an Average Test Loss of 11.0147 on a 299:700 split.The high Average Test Loss value indicates the model's difficulty in minimizing prediction errors, which may be caused by the model's mismatch with the data patterns or overfitting. To improve the model's performance, it is recommended to improve the quality of the training data, optimize the parameters, and apply regularization techniques.