EmoSpeech: A Corpus of Emotionally Rich and Contextually Detailed Speech Annotations

TL;DR

This paper introduces EmoSpeech, a richly annotated emotional speech database created using a generative model to extract and describe speech segments, enabling more nuanced emotional control in TTS systems.

Contribution

It presents a novel automated method for building emotionally rich speech databases with detailed natural language annotations, reducing manual effort and increasing emotional granularity.

Findings

Enhanced emotional granularity in speech database

Reduced reliance on manual annotations

Scalable and cost-effective data augmentation

Abstract

Advances in text-to-speech (TTS) technology have significantly improved the quality of generated speech, closely matching the timbre and intonation of the target speaker. However, due to the inherent complexity of human emotional expression, the development of TTS systems capable of controlling subtle emotional differences remains a formidable challenge. Existing emotional speech databases often suffer from overly simplistic labelling schemes that fail to capture a wide range of emotional states, thus limiting the effectiveness of emotion synthesis in TTS applications. To this end, recent efforts have focussed on building databases that use natural language annotations to describe speech emotions. However, these approaches are costly and require more emotional depth to train robust systems. In this paper, we propose a novel process aimed at building databases by systematically extracting emotion-rich speech segments and annotating them with detailed natural language descriptions through a generative model. This approach enhances the emotional granularity of the database and significantly reduces the reliance on costly manual annotations by automatically augmenting the data with high-level language models. The resulting rich database provides a scalable and economically viable solution for developing a more nuanced and dynamic basis for developing emotionally controlled TTS systems.