Papers by Dingdong Wang
Language-Codec: Bridging Discrete Codec Representations and Speech Language Models (2025.acl-long)
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Shengpeng Ji, Minghui Fang, Jialong Zuo, Ziyue Jiang, Dingdong Wang, Hanting Wang, Hai Huang, Zhou Zhao
| Challenge: | Existing gaps between discrete acoustic codecs and downstream speech language models . initial channel of codebooks contains excessive information, making it difficult to generate tokens from weakly supervised signals such as text. |
| Approach: | They propose a discrete acoustic codec for generating acustic tokens from weakly supervised signals. |
| Outcome: | The proposed language-codec outperforms competing audio compression algorithms and validates on downstream speech language models. |
SocialCC: Interactive Evaluation for Cultural Competence in Language Agents (2025.acl-long)
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| Challenge: | Existing studies have evaluated cultural knowledge of large language models, but they fail to assess dynamic cultural competence. |
| Approach: | They propose a benchmark to assess cultural competence through intercultural scenarios that span 60 countries across six continents. |
| Outcome: | The proposed benchmark measures the ability of large language models to apply cultural knowledge effectively in cross-cultural interactions. |
Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs (2025.emnlp-main)
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| Challenge: | Speech Large Language Models (SpeechLLMs) have emerged as dominant speech processing approaches. |
| Approach: | They compare self-supervised learning-based discrete and continuous features . they compare performance across six spoken language understanding-related tasks . |
| Outcome: | The proposed models outperform discrete tokens and continuous features in six spoken language understanding-related tasks. |
InSerter: Speech Instruction Following with Unsupervised Interleaved Pre-training (2025.acl-long)
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Dingdong Wang, Jin Xu, Ruihang Chu, Zhifang Guo, Xiong Wang, Jincenzi Wu, Dongchao Yang, Shengpeng Ji, Junyang Lin
| Challenge: | Recent advances in speech large language models exhibit suboptimal performance in adhering to speech instructions. |
| Approach: | They propose a method to pre-train large-scale unsupervised speech-text sequences . they use text-to-speech conversion to generate textual continuations corresponding to provided speech segments . |
| Outcome: | The proposed model achieves superior or competitive results across diverse speech processing tasks. |