  Text-to-speech (TTS) development for African languages such as Luganda is
still limited, primarily due to the scarcity of high-quality, single-speaker
recordings essential for training TTS models. Prior work has focused on
utilizing the Luganda Common Voice recordings of multiple speakers aged between
20-49. Although the generated speech is intelligible, it is still of lower
quality than the model trained on studio-grade recordings. This is due to the
insufficient data preprocessing methods applied to improve the quality of the
Common Voice recordings. Furthermore, speech convergence is more difficult to
achieve due to varying intonations, as well as background noise. In this paper,
we show that the quality of Luganda TTS from Common Voice can improve by
training on multiple speakers of close intonation in addition to further
preprocessing of the training data. Specifically, we selected six female
speakers with close intonation determined by subjectively listening and
comparing their voice recordings. In addition to trimming out silent portions
from the beginning and end of the recordings, we applied a pre-trained speech
enhancement model to reduce background noise and enhance audio quality. We also
utilized a pre-trained, non-intrusive, self-supervised Mean Opinion Score (MOS)
estimation model to filter recordings with an estimated MOS over 3.5,
indicating high perceived quality. Subjective MOS evaluations from nine native
Luganda speakers demonstrate that our TTS model achieves a significantly better
MOS of 3.55 compared to the reported 2.5 MOS of the existing model. Moreover,
for a fair comparison, our model trained on six speakers outperforms models
trained on a single-speaker (3.13 MOS) or two speakers (3.22 MOS). This
showcases the effectiveness of compensating for the lack of data from one
speaker with data from multiple speakers of close intonation to improve TTS
quality.
