A system for adapting an emotional text-to-speech model, the system comprising: at least one processor ; and memory , operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor , cause the at least one processor to: receive training examples comprising speech input ; receive labelling data comprising emotion information associated with the speech input ; extract audio signal vectors from the training examples ; and adapt a voice font model based on the audio signal vectors and the labelling data to generate an emotion-adapted voice font model ; characterized in that the processor is further configured to: generate first prosody annotations for the speech input of the training examples ; generate second prosody annotations from the labelling data of the training examples , wherein the second prosody annotations are generated by generating synthesized speech from the labelling data and then annotating the synthesized speech ; determine differences between the first prosody annotations and the second prosody annotations ; and generate a prosody model based on the determined differences between the first prosody annotations and the second prosody annotations . at least one processor ; and memory , operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor , cause the at least one processor to: receive training examples comprising speech input ; receive labelling data comprising emotion information associated with the speech input ; extract audio signal vectors from the training examples ; and adapt a voice font model based on the audio signal vectors and the labelling data to generate an emotion-adapted voice font model ; receive training examples comprising speech input ; receive labelling data comprising emotion information associated with the speech input ; extract audio signal vectors from the training examples ; and adapt a voice font model based on the audio signal vectors and the labelling data to generate an emotion-adapted voice font model ; characterized in that the processor is further configured to: generate first prosody annotations for the speech input of the training examples ; generate second prosody annotations from the labelling data of the training examples , wherein the second prosody annotations are generated by generating synthesized speech from the labelling data and then annotating the synthesized speech ; determine differences between the first prosody annotations and the second prosody annotations ; and generate a prosody model based on the determined differences between the first prosody annotations and the second prosody annotations . generate first prosody annotations for the speech input of the training examples ; generate second prosody annotations from the labelling data of the training examples , wherein the second prosody annotations are generated by generating synthesized speech from the labelling data and then annotating the synthesized speech ; determine differences between the first prosody annotations and the second prosody annotations ; and generate a prosody model based on the determined differences between the first prosody annotations and the second prosody annotations . <EOS>