DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis
Abstract
This paper presents DAIEN-TTS, a zero-shot text-to-speech (TTS) framework that enables ENvironment-aware synthesis through Disentangled Audio Infilling. By leveraging separate speaker and environment prompts, DAIEN-TTS allows independent control over the timbre and the background environment of the synthesized speech. Built upon F5-TTS, the proposed DAIEN-TTS first incorporates a pretrained speech-environment separation (SES) module to disentangle the environmental speech into mel-spectrograms of clean speech and environment audio. Two random span masks of varying lengths are then applied to both mel-spectrograms, which, together with the text embedding, serve as conditions for infilling the masked environmental mel-spectrogram, enabling the simultaneous continuation of personalized speech and time-varying environmental audio. To further enhance controllability during inference, we adopt dual class-free guidance (DCFG) for the speech and environment components and introduce a signal-to-noise ratio (SNR) adaptation strategy to align the synthesized speech with the environment prompt. Experimental results demonstrate that DAIEN-TTS generates environmental personalized speech with high naturalness, strong speaker similarity, and high environmental fidelity.
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 main contribution of this paper is the introduction of DAIEN-TTS, an innovative environment-aware zero-shot TTS framework that enables disentangled control of speaker timbre and background environments, significantly advancing the capabilities of text-to-speech synthesis. The methodology and experimental results demonstrate a meaningful step forward in the field, with potential applications that could reshape user interactions with synthesized speech.
Methodology
The proposed DAIEN-TTS framework introduces a novel approach to zero-shot TTS by utilizing disentangled audio infilling, which allows for independent control over speaker timbre and environmental background. The incorporation of a pretrained speech-environment separation (SES) module is a significant methodological advancement, as it effectively disentangles the speech and environmental components. The use of random span masking during training and dual class-free guidance (DCFG) during inference enhances the model's controllability and adaptability to varying conditions. The methodology is well-structured, leveraging existing frameworks like F5-TTS while innovating on top of them, showcasing a clear progression in the field of TTS synthesis.
Results & evaluation
The experiments are comprehensive, utilizing the LibriTTS corpus and the DNS-Challenge dataset to simulate a variety of environmental conditions. The evaluation metrics include both objective measures (WER, SIM-o) and subjective assessments (MOS for naturalness, speaker similarity, and environment similarity), providing a robust framework for assessing the model's performance. The results demonstrate that DAIEN-TTS outperforms existing baselines, including F5-TTS, in both silence and background environment scenarios, indicating its effectiveness in generating high-quality, environment-aware speech. The thoroughness of the experimental setup and the clarity of the results contribute positively to the paper's impact.
Reproducibility
The paper provides detailed descriptions of the model architecture, training procedures, and evaluation metrics, which are essential for reproducibility. However, specific hyperparameters and the exact configurations of the training environment (e.g., GPU specifications) are mentioned but could be elaborated further to enhance reproducibility. The authors could also consider providing a code repository to facilitate implementation by other researchers.
Limitations
One limitation of the study is the reliance on the LibriTTS corpus, which may not fully capture the diversity of real-world speech and environmental conditions. Additionally, while the model shows strong performance in controlled settings, its robustness in highly variable real-world scenarios remains to be tested. The paper does not address potential biases in the training data, which could affect the generalizability of the model.
Broader impact
The DAIEN-TTS framework has significant implications for applications in virtual reality, audiobooks, and personalized voice assistants, where the ability to synthesize speech with varying environmental contexts can enhance user experience. The ability to independently control speaker characteristics and background environments could lead to more immersive and realistic interactions in various multimedia applications. The research also contributes to the broader field of speech synthesis by addressing the challenge of environment-aware synthesis, paving the way for future advancements in TTS technologies. The main contribution of this paper is the introduction of DAIEN-TTS, an innovative environment-aware zero-shot TTS framework that enables disentangled control of speaker timbre and background environments, significantly advancing the capabilities of text-to-speech synthesis. The methodology and experimental results demonstrate a meaningful step forward in the field, with potential applications that could reshape user interactions with synthesized speech.
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