From the 2 of 30 linked papers with an AI index.
20 papers · 1 filter
ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era
Masao Someki, Alexander Polok, Carlos Carvalho +14
Recent speech research involves increasingly large datasets, complex models, and diverse experimental workflows. However, existing frameworks require substantial engineering effort…
Grounding Spoken LLMs in Multi-Speaker Audio via Diarization Conditioning
Alexander Polok, Samuele Cornell, Sathvik Udupa +3
We propose diarization-conditioned spoken language models (SLMs), a strategy for extending SLMs to far-field multi-talker audio. Rather than adapting the decoder via Serialized Out…
Exploiting Noise Inseparability for Weakly-Supervised Discriminative Speech Denoising Using Noisy Targets
Matthew Maciejewski, Samuele Cornell
Speech denoising is an often necessary step not only for human listening, but also for downstream processing by systems lacking robustness to noisy, real-world acoustic conditions.…
Cross-Talk Speech Reduction, by Separation, for Separation
Zhong-Qiu Wang, Samuele Cornell
In conversational speech separation and recognition tasks, close-talk microphones are typically attached to each speaker during training data collection to capture near-field, clos…
Ring Mixing with Auxiliary Signal-to-Consistency-Error Ratio Loss for Unsupervised Denoising in Speech Separation
Matthew Maciejewski, Samuele Cornell
Noisy speech separation systems are typically trained on fully-synthetic mixtures, limiting generalization to real-world scenarios. Though training on mixtures of in-domain (thus o…
MAPSS: Manifold-based Assessment of Perceptual Source Separation
Amir Ivry, Samuele Cornell, Shinji Watanabe
Objective assessment of audio source-separation systems still mismatches subjective human perception, especially when interference from competing talkers and distortion of the targ…