Papers by Keqi Chen
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching (2025.acl-long)
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| Challenge: | Recent research in Text-to-Speech (TTS) has experienced great advancement . current models can synthesize speech for any given text and mimic the speaker of audio prompt. |
| Approach: | They propose a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT) without complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then denoising is performed for speech generation. |
| Outcome: | The proposed system achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based models. |
Beyond Surface Simplicity: Revealing Hidden Reasoning Attributes for Precise Commonsense Diagnosis (2025.acl-long)
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| Challenge: | Existing commonsense question answering benchmarks often treat these aspects in isolation, resulting in evaluation accuracy differences of up to 24.8% across different difficulty levels. |
| Approach: | They propose a framework that reveals hidden reasoning attributes behind commonsense questions by leveraging the knowledge generated during the reasoning process. |
| Outcome: | The proposed framework reveals hidden reasoning attributes behind commonsense questions by leveraging the knowledge generated during the reasoning process. |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
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Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
SimulS2S-LLM: Unlocking Simultaneous Inference of Speech LLMs for Speech-to-Speech Translation (2025.acl-long)
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| Challenge: | Simultaneous speech translation (SST) outputs translations in parallel with streaming speech input, balancing translation quality and latency. |
| Approach: | They propose to train LLMs offline and employ a test-time policy to guide simultaneous inference by extracting boundary-aware speech prompts that allow it to be better matched with text input data. |
| Outcome: | The proposed model trains speech LLMs offline and employs a test-time policy to guide simultaneous inference. |