CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech

TL;DR

CapSpeech introduces a large, comprehensive benchmark dataset for style-captioned text-to-speech synthesis, enabling improved research and development of diverse, high-fidelity speech synthesis applications.

Contribution

We present CapSpeech, the largest dataset for CapTTS with diverse annotations and new datasets for specific tasks, facilitating advanced research in style-captioned TTS applications.

Findings

High-fidelity, intelligible speech synthesis achieved across styles

CapSpeech dataset is the largest with extensive annotations

Experiments reveal challenges and insights in CapTTS development

Abstract

Recent advancements in generative artificial intelligence have significantly transformed the field of style-captioned text-to-speech synthesis (CapTTS). However, adapting CapTTS to real-world applications remains challenging due to the lack of standardized, comprehensive datasets and limited research on downstream tasks built upon CapTTS. To address these gaps, we introduce CapSpeech, a new benchmark designed for a series of CapTTS-related tasks, including style-captioned text-to-speech synthesis with sound events (CapTTS-SE), accent-captioned TTS (AccCapTTS), emotion-captioned TTS (EmoCapTTS), and text-to-speech synthesis for chat agent (AgentTTS). CapSpeech comprises over 10 million machine-annotated audio-caption pairs and nearly 0.36 million human-annotated audio-caption pairs. In addition, we introduce two new datasets collected and recorded by a professional voice actor and experienced audio engineers, specifically for the AgentTTS and CapTTS-SE tasks. Alongside the datasets, we conduct comprehensive experiments using both autoregressive and non-autoregressive models on CapSpeech. Our results demonstrate high-fidelity and highly intelligible speech synthesis across a diverse range of speaking styles. To the best of our knowledge, CapSpeech is the largest available dataset offering comprehensive annotations for CapTTS-related tasks. The experiments and findings further provide valuable insights into the challenges of developing CapTTS systems.