A system for adapting an emotional text-to-speech model, the system comprising: at least one processor (1302, 1460); and memory (1304, 1462), operatively connected to the at least one processor (1302, 1460) and storing instructions that, when executed by the at least one processor (1302, 1460), cause the at least one processor to: receive training examples (1000, 1200) comprising speech input (1002, 1202); receive labelling data (1004, 1204) comprising emotion information associated with the speech input (1002, 1202); extract audio signal vectors from the training examples (1000, 1200); and adapt a voice font model based on the audio signal vectors and the labelling data (1004, 1204) to generate an emotion-adapted voice font model; characterized in that the processor (1302, 1460) is further configured to: generate first prosody annotations for the speech input (1002, 1202) of the training examples (1000, 1200); generate second prosody annotations from the labelling data (1004, 1204) of the training examples (1000, 1200), wherein the second prosody annotations are generated by generating synthesized speech from the labelling data (1004, 1204) 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 (1302, 1460); and memory (1304, 1462), operatively connected to the at least one processor (1302, 1460) and storing instructions that, when executed by the at least one processor (1302, 1460), cause the at least one processor to: receive training examples (1000, 1200) comprising speech input (1002, 1202); receive labelling data (1004, 1204) comprising emotion information associated with the speech input (1002, 1202); extract audio signal vectors from the training examples (1000, 1200); and adapt a voice font model based on the audio signal vectors and the labelling data (1004, 1204) to generate an emotion-adapted voice font model; receive training examples (1000, 1200) comprising speech input (1002, 1202); receive labelling data (1004, 1204) comprising emotion information associated with the speech input (1002, 1202); extract audio signal vectors from the training examples (1000, 1200); and adapt a voice font model based on the audio signal vectors and the labelling data (1004, 1204) to generate an emotion-adapted voice font model; characterized in that the processor (1302, 1460) is further configured to: generate first prosody annotations for the speech input (1002, 1202) of the training examples (1000, 1200); generate second prosody annotations from the labelling data (1004, 1204) of the training examples (1000, 1200), wherein the second prosody annotations are generated by generating synthesized speech from the labelling data (1004, 1204) 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 (1002, 1202) of the training examples (1000, 1200); generate second prosody annotations from the labelling data (1004, 1204) of the training examples (1000, 1200), wherein the second prosody annotations are generated by generating synthesized speech from the labelling data (1004, 1204) 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.