OV-InstructTTS: Towards Open-Vocabulary Instruct Text-to-Speech
Abstract
Instruct Text-to-Speech (InstructTTS) leverages natural language descriptions as style prompts to guide speech synthesis. However, existing InstructTTS methods mainly rely on a direct combination of audio-related labels or their diverse rephrasings, making it difficult to handle flexible, high-level instructions. Such rigid control is insufficient for users such as content creators who wish to steer generation with descriptive instructions. To address these constraints, we introduce OV-InstructTTS, a new paradigm for open-vocabulary InstructTTS. We propose a comprehensive solution comprising a newly curated dataset, OV-Speech, and a novel reasoning-driven framework. The OV-Speech dataset pairs speech with open-vocabulary instructions, each augmented with a reasoning process that connects high-level instructions to acoustic features. The reasoning-driven framework infers emotional, acoustic, and paralinguistic information from open-vocabulary instructions before synthesizing speech. Evaluations show that this reasoning-driven approach significantly improves instruction-following fidelity and speech expressiveness. We believe this work can inspire the next user-friendly InstructTTS systems with stronger generalization and real-world applicability. The dataset and demos are publicly available on our project page.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces OV-InstructTTS, a novel paradigm that enhances InstructTTS systems by enabling flexible speech synthesis from open-vocabulary instructions through a reasoning-driven framework. This work significantly advances the state of the art in TTS, providing a comprehensive solution that combines a new dataset and innovative methodology, with promising results that could influence future research and applications in the field.
Methodology
The methodology presented in this paper is robust, introducing a novel reasoning-driven framework that effectively connects high-level open-vocabulary instructions to low-level acoustic features. The construction of the OV-Speech dataset is methodical, emphasizing the importance of contextual information and reasoning chains, which are critical for improving the expressiveness and fidelity of synthesized speech. The use of large language models (LLMs) to generate instructions and reasoning chains demonstrates an innovative approach to bridging the semantic gap in TTS systems.
Results & evaluation
The experimental evaluation is thorough, utilizing both objective and subjective metrics to assess the performance of the proposed model against several strong baselines. The results indicate significant improvements in instruction following and speech naturalness, which are well-supported by the ablation studies. The comprehensive evaluation methodology enhances the credibility of the findings.
Reproducibility
The paper provides sufficient implementation details, including model architecture, training parameters, and dataset partitioning, which facilitate reproducibility. However, the lack of specific information about the datasets used for baseline comparisons could hinder full reproducibility for those models.
Limitations
One limitation is the reliance on LLMs for generating reasoning chains and instructions, which may introduce biases or inaccuracies depending on the model's training data. Additionally, the dataset's quality and diversity may affect the generalizability of the results. The paper does not address potential ethical concerns related to the use of AI-generated speech in sensitive applications.
Broader impact
This research has significant implications for the field of TTS, particularly in enhancing user control and expressiveness in speech synthesis. The proposed system could benefit various applications, including content creation, virtual assistants, and accessibility tools, making TTS more intuitive and user-friendly. The open-vocabulary approach could lead to more personalized and engaging interactions between users and machines. The paper introduces OV-InstructTTS, a novel paradigm that enhances InstructTTS systems by enabling flexible speech synthesis from open-vocabulary instructions through a reasoning-driven framework. This work significantly advances the state of the art in TTS, providing a comprehensive solution that combines a new dataset and innovative methodology, with promising results that could influence future research and applications in the field.
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