Expressive Text-to-speech Using Style Tag

Abstract

As recent text-to-speech (TTS) systems have been rapidly improved in speech quality and generation speed, many researchers now focus on a more challenging issue: expressive TTS. To control speaking styles, existing expressive TTS models use categorical style index or reference speech as style input. In this work, we propose StyleTagging-TTS (ST-TTS), a novel expressive TTS model that utilizes a style tag written in natural language. Using a style-tagged TTS dataset and a pre-trained language model, we modeled the relationship between linguistic embedding and speaking style domain, which enables our model to work even with style tags unseen during training. As style tag is written in natural language, it can control speaking style in a more intuitive, interpretable, and scalable way compared with style index or reference speech. In addition, in terms of model architecture, we propose an efficient non-autoregressive (NAR) TTS architecture with single-stage training. The experimental resu

Stats

• citations27
• S2 citations—
• github stars0
• HF likes0
• heat score10.85
• arxiv keykim2021expressive

Related papers

• Instructtts: Modelling Expressive TTS In Discrete Latent Space With Natural Language Style Prompt (2023)0.00
• Styletts: A Style-based Generative Model For Natural And Diverse Text-to-speech Synthesis (2022)10.97
• Expressive TTS Driven By Natural Language Prompts Using Few Human Annotations (2023)0.00
• Self-supervised Context-aware Style Representation For Expressive Speech Synthesis (2022)6.34
• Text-driven Emotional Style Control And Cross-speaker Style Transfer In Neural TTS (2022)7.81
• STYLER: Style Factor Modeling With Rapidity And Robustness Via Speech Decomposition For Expressive And Controllable Neural Text To Speech (2021)9.23
• Autostyle-tts: Retrieval-augmented Generation Based Automatic Style Matching Text-to-speech Synthesis (2025)4.52
• Fine-grained Style Control In Transformer-based Text-to-speech Synthesis (2021)11.19