  State-of-the-art speech synthesis models try to get as close as possible to
the human voice. Hence, modelling emotions is an essential part of
Text-To-Speech (TTS) research. In our work, we selected FastSpeech2 as the
starting point and proposed a series of modifications for synthesizing
emotional speech. According to automatic and human evaluation, our model,
EmoSpeech, surpasses existing models regarding both MOS score and emotion
recognition accuracy in generated speech. We provided a detailed ablation study
for every extension to FastSpeech2 architecture that forms EmoSpeech. The
uneven distribution of emotions in the text is crucial for better, synthesized
speech and intonation perception. Our model includes a conditioning mechanism
that effectively handles this issue by allowing emotions to contribute to each
phone with varying intensity levels. The human assessment indicates that
proposed modifications generate audio with higher MOS and emotional
expressiveness.
