SpeechEditBench provides seven atomic editing tasks, compositional multi-operation instructions, and an anchor-based protocol yielding target success, preservation success, and joint success metrics; evaluations show no model excels across dimensions and compositional editing is especially difficult
Mixed citations
Liu, ”Zero-shot Voice Conversion with Diffusion Transformers,”
Mixed citation behavior. Most common role is background (60%).
11
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60% of classified citations
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method 1
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UNVERDICTED 11representative citing papers
Poly-SVC converts singing voices from polyphonic recordings while keeping melody, lyrics, and harmonies by combining CQT-based pitch extraction with a conditional flow matching diffusion decoder.
X-VC achieves zero-shot streaming voice conversion via one-step codec-space conversion with dual-conditioning acoustic converter and role-assignment training on generated paired data.
Voice conversion in interactive studies boosts user trust in SpeechLLM responses while automated metrics detect accent-by-gender disparities in alignment and verbosity.
RTCFake is the first large-scale dataset of real-time communication speech deepfakes paired with offline versions, paired with a phoneme-guided consistency learning method that improves cross-platform and noise-robust detection.
Current video models succeed on basic understanding but achieve under 25% success on logically grounded generation and near 0% on interactive generation, exposing gaps in multimodal reasoning.
KNN retrieval over WavLM representations creates synthetic source-target pairs from non-parallel data for supervised voice conversion training with a speaker loss, achieving strong results on multilingual test sets despite English-only training.
Kimi-Audio is an open-source audio foundation model that achieves state-of-the-art results on speech recognition, audio understanding, question answering, and conversation after pre-training on more than 13 million hours of speech, sound, and music data.
Unified guidance framework for Flow Matching speech synthesis achieves nearly 3x faster inference and improved speaker similarity by combining heterogeneous data augmentation with intrinsic model guidance to eliminate CFG overhead.
AT-ADD introduces standardized tracks and datasets for evaluating audio deepfake detectors on speech under real-world conditions and on diverse unknown audio types to promote generalization beyond speech-centric methods.
A holistic survey of affective computing for intelligent agents covering emotion understanding via multimodal data, affective cognition, emotional expression synthesis, key challenges, and future directions emphasizing generative technologies.
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Kimi-Audio Technical Report
Kimi-Audio is an open-source audio foundation model that achieves state-of-the-art results on speech recognition, audio understanding, question answering, and conversation after pre-training on more than 13 million hours of speech, sound, and music data.