  The field of prosody transfer in speech synthesis systems is rapidly
advancing. This research is focused on evaluating learning methods for adapting
pre-trained monolingual text-to-speech (TTS) models to multilingual conditions,
i.e., Supervised Fine-Tuning (SFT) and Transfer Learning (TL). This comparison
utilizes three distinct metrics: Mean Opinion Score (MOS), Recognition Accuracy
(RA), and Mel Cepstral Distortion (MCD). Results demonstrate that, in
comparison to SFT, TL leads to significantly enhanced performance, with an
average MOS higher by 1.53 points, a 37.5% increase in RA, and approximately a
7.8-point improvement in MCD. These findings are instrumental in helping build
TTS models for low-resource languages.
