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We propose StyleCap, a method to generate natural language descriptions ofspeaking styles appearing in speech. Although most of conventional techniquesfor para-/non-linguistic information recognition focus on the categoryclassification or the intensity estimation of pre-defined labels, they cannotprovide the reasoning of the recognition result in an interpretable manner.StyleCap is a first step towards an end-to-end method for generatingspeaking-style prompts from speech, i.e., automatic speaking-style captioning.StyleCap is trained with paired data of speech and natural languagedescriptions. We train neural networks that convert a speech representationvector into prefix vectors that are fed into a large language model (LLM)-basedtext decoder. We explore an appropriate text decoder and speech featurerepresentation suitable for this new task. The experimental results demonstratethat our StyleCap leveraging richer LLMs for the text decoder, speechself-supervised learning (SSL) features, and sentence rephrasing augmentationimproves the accuracy and diversity of generated speaking-style captions.Samples of speaking-style captions generated by our StyleCap are publiclyavailable.

StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models