Adversarial Learning for Duration Prediction in Indonesian Text-to-Speech: Modification to Stochastic and Deterministic Predictors

Text-to-Speech (TTS) technology has significantly progressed with deep learning, especially through models like Variational Autoencoder with Adversarial Learning for End-toEnd Text-to-Speech (VITS). However, improving audio quality particularly in duration diversity remains a challenge, especially for languages like Indonesian due to limited datasets and research. This study compares the performance of VITS using Stochastic Duration Predictor (SDP) and Deterministic Duration Predictor (DDP), while also exploring the impact of adversarial training on duration prediction. Evaluation employed subjective Mean Opinion Score (MOS) and objective Cosine Similarity using Resemblyzer. Two datasets were used: 343 formal audio samples and 1250 mixed (formal and informal) samples. The more diverse dataset achieved better results, with a cosine similarity of 0.91124 and a MOS of 4.54. Findings indicate that SDP produces more natural durations, and adversarial learning enhances audio quality through better duration modeling.