268 citations · 1.2k across the 23 of their papers we have counts for
6 papers · 1 filter
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Joel Shor, Aren Jansen, Wei Han +2
Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishin…
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, Daniel S. Park, Wei Han +23
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a m…
Attention Bottlenecks for Multimodal Fusion
Arsha Nagrani, Shan Yang, Anurag Arnab +3
Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. Machine perception models, in stark contr…
Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation
Scott Wisdom, Aren Jansen, Ron J. Weiss +2
Supervised neural network training has led to significant progress on single-channel sound separation. This approach relies on ground truth isolated sources, which precludes scalin…
The Benefit Of Temporally-Strong Labels In Audio Event Classification
Shawn Hershey, Daniel P W Ellis, Eduardo Fonseca +4
To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (~0.1 sec resolution) "strong" labels for a portion of the AudioSet dataset.…
Self-Supervised Learning from Automatically Separated Sound Scenes
Eduardo Fonseca, Aren Jansen, Daniel P. W. Ellis +7
Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings. The associati…