Mechanisms of Multimodal Synchronization: Insights from Decoder-Based Video-Text-to-Speech Synthesis
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910147437133824 |
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| author | Gupta, Akshita Likhomanenko, Tatiana Yang, Karren Dai Bai, Richard He Aldeneh, Zakaria Jaitly, Navdeep |
| author_facet | Gupta, Akshita Likhomanenko, Tatiana Yang, Karren Dai Bai, Richard He Aldeneh, Zakaria Jaitly, Navdeep |
| contents | Unified decoder-only transformers have shown promise for multimodal generation, yet the mechanisms by which they synchronize modalities with heterogeneous sampling rates remain underexplored. We investigate these mechanisms through video-text-to-speech (VTTS) synthesis-a controlled task requiring fine-grained temporal alignment between sparse text, video, and continuous speech. Using a unified decoder-only transformer, dubbed Visatronic, trained on VoxCeleb2, we study: (i) how modalities contribute complementary information, (ii) how positional encoding strategies enable synchronization across heterogeneous rates, (iii) how modality ordering shapes the trade-off between in-domain performance and cross-domain transfer, (iv) how phoneme-level synchronization metrics provide diagnostic insight into per-phoneme timing errors. Our findings reveal that both "global sequential indexing'' (unique position IDs across modalities) and "co-temporal ordered indexing'' (identical IDs for temporally corresponding tokens) achieve strong synchronization performance, with co-temporal ordered indexing providing a simple mechanism without explicit timestamp metadata. Both text and video contribute complementary signals: text ensures intelligibility while video provides temporal cues and emotional expressiveness. Modality ordering reveals a consistent trade-off: video-first ordering achieves stronger in-domain performance while text-first ordering generalizes more robustly to unseen domains. Our findings also reveal, that diverse large-scale training enables transferable synchronization strategies. To enable fine-grained analysis, we also introduce TimeSync, a phoneme-level metric that reveals temporal misalignments overlooked by frame-level metrics. These insights establish VTTS as a valuable testbed for understanding temporal synchronization in unified multimodal decoders. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_17690 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mechanisms of Multimodal Synchronization: Insights from Decoder-Based Video-Text-to-Speech Synthesis Gupta, Akshita Likhomanenko, Tatiana Yang, Karren Dai Bai, Richard He Aldeneh, Zakaria Jaitly, Navdeep Multimedia Computer Vision and Pattern Recognition Sound Audio and Speech Processing Unified decoder-only transformers have shown promise for multimodal generation, yet the mechanisms by which they synchronize modalities with heterogeneous sampling rates remain underexplored. We investigate these mechanisms through video-text-to-speech (VTTS) synthesis-a controlled task requiring fine-grained temporal alignment between sparse text, video, and continuous speech. Using a unified decoder-only transformer, dubbed Visatronic, trained on VoxCeleb2, we study: (i) how modalities contribute complementary information, (ii) how positional encoding strategies enable synchronization across heterogeneous rates, (iii) how modality ordering shapes the trade-off between in-domain performance and cross-domain transfer, (iv) how phoneme-level synchronization metrics provide diagnostic insight into per-phoneme timing errors. Our findings reveal that both "global sequential indexing'' (unique position IDs across modalities) and "co-temporal ordered indexing'' (identical IDs for temporally corresponding tokens) achieve strong synchronization performance, with co-temporal ordered indexing providing a simple mechanism without explicit timestamp metadata. Both text and video contribute complementary signals: text ensures intelligibility while video provides temporal cues and emotional expressiveness. Modality ordering reveals a consistent trade-off: video-first ordering achieves stronger in-domain performance while text-first ordering generalizes more robustly to unseen domains. Our findings also reveal, that diverse large-scale training enables transferable synchronization strategies. To enable fine-grained analysis, we also introduce TimeSync, a phoneme-level metric that reveals temporal misalignments overlooked by frame-level metrics. These insights establish VTTS as a valuable testbed for understanding temporal synchronization in unified multimodal decoders. |
| title | Mechanisms of Multimodal Synchronization: Insights from Decoder-Based Video-Text-to-Speech Synthesis |
| topic | Multimedia Computer Vision and Pattern Recognition Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2411.17690 |