Papers › Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit...
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
Recently, speech-text pre-training methods have shown remarkable success in many speech and natural language processing tasks. However, most previous pre-trained models are usually tailored for one or two specific tasks, but fail to conquer a wide range of speech-text tasks. In addition, existing speech-text pre-training methods fail to explore the contextual information within a dialogue to enrich utterance representations. In this paper, we propose Speech-text dialog Pre-training for spoken dialog understanding with ExpliCiT cRoss-Modal Alignment (SPECTRA), which is the first-ever speech-text dialog pre-training model. Concretely, to consider the temporality of speech modality, we design a novel temporal position prediction task to capture the speech-text alignment. This pre-training task aims to predict the start and end time of each textual word in the corresponding speech waveform. In addition, to learn the characteristics of spoken dialogs, we generalize a response selection task from textual dialog pre-training to speech-text dialog pre-training scenarios. Experimental results on four different downstream speech-text tasks demonstrate the superiority of SPECTRA in learning speech-text alignment and multi-turn dialog context.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | IEMOCAP | SPECTRA | Accuracy | 67.94 | #59 of 59 | Archive leaderboard | report |
| Multimodal Intent Recognition | MIntRec | SPECTRA | Accuracy (20 classes) | 73.48 | #3 of 6 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | CMU-MOSEI | SPECTRA | Accuracy | 87.34 | #4 of 15 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | CMU-MOSI | SPECTRA | Acc-2 | 87.5 | #11 of 12 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | MOSI | SPECTRA | Accuracy | 87.50 | #2 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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