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

TL;DR

The EMNS corpus provides a high-quality, emotive speech dataset with labeled emotional states, enhancing naturalness and expressiveness in text-to-speech applications for narrative storytelling in media.

Contribution

It introduces a novel, labeled emotive speech dataset with high authenticity, supporting improved emotional expressiveness in TTS systems for storytelling.

Findings

Achieved highest scores in emotion conveyance and expressiveness

Outperformed other datasets in conveying shared emotions

Participants recognized recordings as genuine and expressive

Abstract

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.