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The increasing adoption of text-to-speech technologies has led to a growingdemand for natural and emotive voices that adapt to a conversation's contextand emotional tone. The Emotive Narrative Storytelling (EMNS) corpus is aunique speech dataset created to enhance conversations' expressiveness andemotive quality in interactive narrative-driven systems. The corpus consists ofa 2.3-hour recording featuring a female speaker delivering labelled utterances.It encompasses eight acted emotional states, evenly distributed with a varianceof 0.68%, along with expressiveness levels and natural language descriptionswith word emphasis labels. The evaluation of audio samples from differentdatasets revealed that the EMNS corpus achieved the highest average scores inaccurately conveying emotions and demonstrating expressiveness. It outperformedother datasets in conveying shared emotions and achieved comparable levels ofgenuineness. A classification task confirmed the accurate representation ofintended emotions in the corpus, with participants recognising the recordingsas genuine and expressive. Additionally, the availability of the datasetcollection tool under the Apache 2.0 License simplifies remote speech datacollection for researchers.

EMNS /Imz/ Corpus: An emotive single-speaker dataset for narrative storytelling in games, television and graphic novels