  Several high-resource Text to Speech (TTS) systems currently produce natural,
well-established human-like speech. In contrast, low-resource languages,
including Arabic, have very limited TTS systems due to the lack of resources.
We propose a fully unsupervised method for building TTS, including automatic
data selection and pre-training/fine-tuning strategies for TTS training, using
broadcast news as a case study. We show how careful selection of data, yet
smaller amounts, can improve the efficiency of TTS system in generating more
natural speech than a system trained on a bigger dataset. We adopt to propose
different approaches for the: 1) data: we applied automatic annotations using
DNSMOS, automatic vowelization, and automatic speech recognition (ASR) for
fixing transcriptions' errors; 2) model: we used transfer learning from
high-resource language in TTS model and fine-tuned it with one hour broadcast
recording then we used this model to guide a FastSpeech2-based Conformer model
for duration. Our objective evaluation shows 3.9% character error rate (CER),
while the groundtruth has 1.3% CER. As for the subjective evaluation, where 1
is bad and 5 is excellent, our FastSpeech2-based Conformer model achieved a
mean opinion score (MOS) of 4.4 for intelligibility and 4.2 for naturalness,
where many annotators recognized the voice of the broadcaster, which proves the
effectiveness of our proposed unsupervised method.
