Optimizing Filipino Text-to-Speech Synthesis: Integration of Generative Models

This paper explored the development of an optimized text-to-speech (TTS) system for Filipino by integrating two advanced generative models: FastSpeech 2 and Tacotron 2. Since Filipino is a low-resource language for TTS, we leveraged the Filipino Speech Corpus—which includes over 150 hours of annotated audio—to create model-specific datasets. Preprocessing steps using the Montreal Forced Aligner, phoneme-level pitch and energy normalization were implemented in FastSpeech2 to ensure precise spectral mapping. In contrast, Tacotron 2 was trained on gender-specific datasets to better capture natural prosody variations. Each synthesis pipeline paired its model with a state- of-the-art vocoder: WaveGlow for Tacotron 2 and a universal HiFi-GAN for FastSpeech 2.Objective evaluation was performed using the mel-cepstral distance metric to compare the spectral characteristics of the synthesized speech against ground truth recordings. Subjective listening tests including Mean Opinion Score (MOS) assessments and AB tests were conducted in a controlled lab environment in which the insights were provided to analyze perceptual naturalness and intelligibility of synthesized speech. Overall, the results indicate that while both models contribute to improving Filipino TTS, the FastSpeech 2 surpassed Tacotron 2 in MCD however Tacotron 2 produced higher MOS ratings and more natural-sounding speech than FastSpeech 2.This study offers a useful framework and insights in enhancing TTS systems despite using low-resource languages, which could help expand access to digital voice applications and guide future research in speech synthesis.