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State-of-the-art speech synthesis models try to get as close as possible tothe human voice. Hence, modelling emotions is an essential part ofText-To-Speech (TTS) research. In our work, we selected FastSpeech2 as thestarting point and proposed a series of modifications for synthesizingemotional speech. According to automatic and human evaluation, our model,EmoSpeech, surpasses existing models regarding both MOS score and emotionrecognition accuracy in generated speech. We provided a detailed ablation studyfor every extension to FastSpeech2 architecture that forms EmoSpeech. Theuneven distribution of emotions in the text is crucial for better, synthesizedspeech and intonation perception. Our model includes a conditioning mechanismthat effectively handles this issue by allowing emotions to contribute to eachphone with varying intensity levels. The human assessment indicates thatproposed modifications generate audio with higher MOS and emotionalexpressiveness.

EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech