  The increasing adoption of text-to-speech technologies has led to a growing
demand for natural and emotive voices that adapt to a conversation's context
and emotional tone. The Emotive Narrative Storytelling (EMNS) corpus is a
unique speech dataset created to enhance conversations' expressiveness and
emotive quality in interactive narrative-driven systems. The corpus consists of
a 2.3-hour recording featuring a female speaker delivering labelled utterances.
It encompasses eight acted emotional states, evenly distributed with a variance
of 0.68%, along with expressiveness levels and natural language descriptions
with word emphasis labels. The evaluation of audio samples from different
datasets revealed that the EMNS corpus achieved the highest average scores in
accurately conveying emotions and demonstrating expressiveness. It outperformed
other datasets in conveying shared emotions and achieved comparable levels of
genuineness. A classification task confirmed the accurate representation of
intended emotions in the corpus, with participants recognising the recordings
as genuine and expressive. Additionally, the availability of the dataset
collection tool under the Apache 2.0 License simplifies remote speech data
collection for researchers.
