INDONESIAN SPONTANEOUS SPEECH RECOGNITION SYSTEM USING DEEP NEURAL NETWORKS

• Main Author: Arif Rahman, Dandy
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/48149
• Institution: Institut Teknologi Bandung

The existing Indonesian speech recognition system has an accuracy that is still not good for spontaneous speech recognition. The system was trained using the HMM-GMM acoustic model. In this study, spontaneous speech data collected in Indonesian with a duration of 14 hours and speech recognition system performance was improved by replacing the acoustic model with a neural network-based model. The neural network topology used are Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Time Delay Neural Network (TDNN). In this paper, the baseline is the HMM-GMM acoustic model trained with dictated speech, the WER obtained by 73.87%. Then the model was trained on data augmented with noise, the WER value dropped to 71.15%. Then the adaptation technique is applied to the model so that the WER drops to 62.75%. Then adaptation model added noise augmentation and WER dropped to 62.16%. In subsequent experiments, the model was trained with mixed training data between dictated and spontaneous speech, the WER value dropped to 57.59%. Furthermore, the acoustic model was replaced with a neural network-based model. In the DNN model, the WER value drops to 50.02%. While on the CNN model, the WER value dropped to 47.58%. The smallest WER value was obtained in acoustic modeling using TDNN topology. The WER value of the model is 40.63%.