Papers by Peiji Yang
TellWhisper: Tell Whisper Who Speaks When (2026.acl-long)
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| Challenge: | Existing approaches decouple temporal modeling and speaker modeling when addressing 'when' and 'who' . a new framework that couples temporal structure with speaker dynamics is proposed to address these limitations . |
| Approach: | They propose a framework that couples temporal and speaker identity within the speech encoder . they propose TS-RoPE, a time-speaker rotary positional encoding that partitions Query/Key channels into temporal, speaker subspaces and applies region-specific rotations to align "when" and "who" cues in selfattention. |
| Outcome: | The proposed framework couples temporal structure with speaker dynamics in speech encoder . it uses frame-level speaker activity to estimate speaker-activity estimates . |
Eliciting Implicit Acoustic Styles from Open-domain Instructions to Facilitate Fine-grained Controllable Generation of Speech (2025.emnlp-main)
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| Challenge: | Current work relies on pre-defined rules or templates to control the style of speech. |
| Approach: | They propose to use open-domain instructions to generate speech with the acoustic style that meets users’ needs based on their instructions. |
| Outcome: | The proposed model can be used to generate speech with the acoustic style that meets users’ needs based on open-domain instructions. |
Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)
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| Challenge: | Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs. |
| Approach: | They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks. |
| Outcome: | The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings. |