Sign-to-Speech Prosody Transfer via Sign Reconstruction-based GAN
Abstract
Deep learning models have improved sign language-to-text translation and made it easier for non-signers to understand signed messages. When the goal is spoken communication, a naive approach is to convert signed messages into text and then synthesize speech via Text-to-Speech (TTS). However, this two-stage pipeline inevitably treat text as a bottleneck representation, causing the loss of rich non-verbal information originally conveyed in the signing. To address this limitation, we propose a novel task, \emph{Sign-to-Speech Prosody Transfer}, which aims to capture the global prosodic nuances expressed in sign language and directly integrate them into synthesized speech. A major challenge is that aligning sign and speech requires expert knowledge, making annotation extremely costly and preventing the construction of large parallel corpora. To overcome this, we introduce \emph{SignRecGAN}, a scalable training framework that leverages unimodal datasets without cross-modal annotations through adversarial learning and reconstruction losses. Furthermore, we propose \emph{S2PFormer}, a new model architecture that preserves the expressive power of existing TTS models while enabling the injection of sign-derived prosody into the synthesized speech. Extensive experiments demonstrate that the proposed method can synthesize speech that faithfully reflects the emotional content of sign language, thereby opening new possibilities for more natural sign language communication. Our code will be available upon acceptance.
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 presents a significant advancement in sign language processing through the introduction of a novel task and a robust methodology that effectively captures prosodic nuances in synthesized speech. The combination of adversarial learning and reconstruction losses represents a meaningful contribution to the field, with potential applications that could greatly enhance communication for the hearing impaired.
Methodology
The paper introduces a novel task of Sign-to-Speech Prosody Transfer, which is a significant advancement in the field of multimodal learning. The methodology employs a GAN-based framework (SignRecGAN) that utilizes unpaired unimodal datasets, thus addressing the challenge of obtaining aligned datasets for sign and speech. The architecture (S2PFormer) effectively integrates sign-derived prosody into synthesized speech, maintaining the expressiveness of TTS models. The use of adversarial learning combined with reconstruction losses (SignRec loss and ProMo loss) is innovative, ensuring that the synthesized speech retains the nuances of sign language. However, the paper could benefit from a more detailed exploration of the limitations of the proposed losses and their impact on the final output.
Results & evaluation
The experimental setup is robust, utilizing both qualitative and quantitative evaluations, including user studies and objective metrics like WER and UTMOS. The paper reports significant findings that demonstrate the effectiveness of the proposed method in capturing emotional nuances in synthesized speech compared to traditional two-stage methods. The ablation studies provide insights into the contributions of each component, reinforcing the importance of the proposed losses. However, the paper lacks a comprehensive comparison with other state-of-the-art methods in the same domain, which could strengthen its claims.
Reproducibility
The paper provides sufficient details on the datasets used, preprocessing steps, and the architecture of the model. However, the absence of a publicly available code repository at the time of review limits reproducibility. The authors mention that the code will be available upon acceptance, which is a positive aspect but should ideally be accessible during the review process.
Limitations
One limitation is the reliance on unimodal datasets, which may not fully capture the complexities of sign language prosody. Additionally, the subjective evaluation metrics, while valuable, may introduce bias depending on the participants' familiarity with sign language. The paper also does not address the potential challenges in scaling the model to different sign languages or dialects.
Broader impact
The proposed method has significant implications for improving communication for individuals with hearing impairments, potentially enhancing the expressiveness and naturalness of synthesized speech in sign language applications. This could lead to better integration of sign language users in various contexts, including education and social interactions. The approach also opens avenues for further research in multimodal learning and prosody transfer, which could benefit other areas of machine learning. The paper presents a significant advancement in sign language processing through the introduction of a novel task and a robust methodology that effectively captures prosodic nuances in synthesized speech. The combination of adversarial learning and reconstruction losses represents a meaningful contribution to the field, with potential applications that could greatly enhance communication for the hearing impaired.
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