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16 papers published across 0 labs.
Audio-language models excel at identifying sounds but fail to grasp the crucial context of how speakers react to them.
Fine-grained emotion control in TTS is now achievable at inference time with minimal speaker or content degradation, bypassing the need for expensive model retraining.
Black-box text-to-music generators can be steered to precisely match complex user intent by an agent that iteratively refines prompts based on rubric-based audio verification.
Latency in voice assistants plummets by 20% by speculatively executing tool calls *during* speech recognition, not after.
Interactive AI worlds can now speak and react in real-time with spatially accurate sound, bridging the sensory gap in generative environments.
Audio-language models excel at identifying sounds but fail to grasp the crucial context of how speakers react to them.
Fine-grained emotion control in TTS is now achievable at inference time with minimal speaker or content degradation, bypassing the need for expensive model retraining.
Black-box text-to-music generators can be steered to precisely match complex user intent by an agent that iteratively refines prompts based on rubric-based audio verification.
Latency in voice assistants plummets by 20% by speculatively executing tool calls *during* speech recognition, not after.
Interactive AI worlds can now speak and react in real-time with spatially accurate sound, bridging the sensory gap in generative environments.
A single-voice TTS model with 10x fewer parameters and 15x less compute than its teacher achieves near-identical audio quality, runnable on a single laptop.
Wrist accelerometers can now yield population-scale sleep heart rate insights, with a novel transformer model outperforming existing methods in estimation availability.
A novel cross-domain recommendation framework achieves state-of-the-art book-to-music mood matching by fusing sentiment embeddings with LLM-driven emotional quadrant classification.
Simple vMF likelihood modeling unlocks robust label-free speaker embedding enhancement, outperforming complex diffusion baselines on challenging acoustic mismatches.
Iterative refinement of a class token with shared blocks slashes transformer parameters and computation while boosting audio recognition accuracy.
Acoustic noise, even when not diagnostically informative, can systematically manipulate Alzheimer's predictions in SSL models, revealing a critical vulnerability in current speech-based assessments.
Zero-shot voice conversion now achieves $9\times$ faster inference with improved perceptual quality by jointly optimizing flow and content encoding.
Most ALMs fail to ground their audio deepfake detection rationales in actual acoustic evidence, despite generating plausible explanations.
Brain-to-text decoding performance gains were an artifact of timing shortcuts, not neural signals, and removing this artifact unlocks substantial improvements with simple methods.
AI-driven music comparison tools can effectively surface subtle, expert-validated differences by letting users precisely scope comparisons and inspect AI-interpreted features.
Immersive AR/VR experiences can finally have stereo audio that dynamically tracks visual sources, thanks to a novel framework that binds visual motion to sound localization.
