514 citations · 853 across the 56 of their papers we have counts for
62 papers
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References
Samuele Cornell, Matthew Maciejewski
Unsupervised speech denoising methods such as MixIT and noisy-target training can be trained directly on noisy recordings, avoiding the sim-to-real gap of synthetic training data,…
YODAS v3: Over 1 Million Hours of High-Bandwidth, Stereophonic, Multilingual Speech
William Chen, Shinnosuke Takamichi, Sayaka Shiota +3
We present YODAS v3, a weakly-labeled speech corpus containing over 1.1 million hours of 48kHz multi-channel audio in 147 languages, released under a CC BY 3.0 license. YODAS v3 is…
LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs
Thanapat Trachu, Samuele Cornell, William Chen +1
Neural audio codecs are a key component in speech language modeling. However, their high frame rates lead to long sequence lengths, increasing computational costs. Dynamic frame ra…
Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens
Daigo Takizawa, Tomohiko Nakamura, Samuele Cornell +3
Neural audio codecs (NACs) have become popular for obtaining speech representations as discrete tokens. Beyond compression, discrete tokens can be used to train self-supervised lea…
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…