Papers with VC
DIALECTIC: A Multi-Agent System for Startup Evaluation (2026.eacl-industry)
Copied to clipboard
| Challenge: | Venture capital (VC) investors face a large number of investment opportunities but only invest in few of them. |
| Approach: | They propose an LLM-based system that gathers factual knowledge about a startup and organizes it into a question tree. |
| Outcome: | The proposed system matches the precision of human VCs in predicting startup success. |
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)
Copied to clipboard
Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao
| Challenge: | Expressive zero-shot voice conversion (VC) aims to modify source timbre to match unseen speaker . existing zero- shot VC systems struggle to reproduce paralinguistic information in highly expressive speech . |
| Approach: | They propose a framework for expressive zero-shot voice conversion that uses hybrid content encoding and memory-augmented context-aware timbre modeling. |
| Outcome: | The proposed framework surpasses state-of-the-art VC systems in speech naturalness, speaker similarity, and speaker similarness. |
StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion (2024.acl-long)
Copied to clipboard
| Challenge: | Existing LM-based VC models require offline conversion from source semantics to acoustic features, limiting their deployment to real-time applications. |
| Approach: | They propose a streaming LM-based model for zero-shot voice conversion that uses a fully causal context-aware LM with a temporal-independent acoustic predictor to facilitate real-time conversion given arbitrary speaker prompts and source speech. |
| Outcome: | The proposed model achieves comparable performance to non-streaming VC systems while maintaining a fully causal context-aware LM with a temporal-independent acoustic predictor. |
Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units (2023.findings-emnlp)
Copied to clipboard
| Challenge: | DISSC is a lightweight voice conversion method that converts the rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. |
| Approach: | They propose a method that converts rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. |
| Outcome: | The proposed method outperforms baseline methods on quantitative and qualitative evaluations. |
Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)
Copied to clipboard
Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao
| Challenge: | Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody. |
| Approach: | They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content. |
| Outcome: | The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm. |
O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Traditional voice conversion methods attempt to separate speaker identity and linguistic information into distinct representations, but this method often leads to information loss during training. |
| Approach: | They propose a method that leverages synthetic speech data generated by a pretrained model . synthetic data pairs that share the same linguistic content are used as input-output pairs . |
| Outcome: | The proposed method outperforms state-of-the-art methods in speaker-to-voice conversions. |
Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction (2026.findings-acl)
Copied to clipboard
| Challenge: | Most venture capital investments fail, while a few deliver outsized returns. |
| Approach: | They propose a framework that synthesizes relational evidence across sources . they propose combining information-gain-driven retriever and knowledge base to ground reasoning . |
| Outcome: | The proposed framework achieves +5.9% F1 and +22.1% Precision@5 over state-of-the-art baselines. |