Toranosuke Manabe

Toranosuke Manabe, Yuto Shibata, Shinnosuke Takamichi et al.

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.

Yuchi Ishikawa, Toranosuke Manabe, Tatsuya Komatsu et al.

Audio-visual captioning aims to generate holistic scene descriptions by jointly modeling sound and vision. While recent methods have improved performance through sophisticated modality fusion, it remains unclear to what extent the two modalities are complementary in current audio-visual captioning models and how robust these models are when one modality is degraded. We address these questions by conducting systematic modality robustness tests on LAVCap, a state-of-the-art audio-visual captioning model, in which we selectively suppress or corrupt the audio or visual streams to quantify sensitivity and complementarity. The analysis reveals a pronounced bias toward the audio stream in LAVCap. To evaluate how balanced audio-visual captioning models are in their use of both modalities, we augment AudioCaps with textual annotations that jointly describe the audio and visual streams, yielding the AudioVisualCaps dataset. In our experiments, we report LAVCap baseline results on AudioVisualCaps. We also evaluate the model under modality robustness tests on AudioVisualCaps and the results indicate that LAVCap trained on AudioVisualCaps exhibits less modality bias than when trained on AudioCaps.