187 citations · 698 across the 55 of their papers we have counts for
6 papers · 1 filter
A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging
Juncheng Li, Yun Wang, Joseph Szurley +2
The lack of strong labels has severely limited the state-of-the-art fully supervised audio tagging systems to be scaled to larger dataset. Meanwhile, audio-visual learning models b…
Multiple Instance Deep Learning for Weakly Supervised Small-Footprint Audio Event Detection
Shao-Yen Tseng, Juncheng Li, Yun Wang +3
State-of-the-art audio event detection (AED) systems rely on supervised learning using strongly labeled data. However, this dependence severely limits scalability to large-scale da…
Subword and Crossword Units for CTC Acoustic Models
Thomas Zenkel, Ramon Sanabria, Florian Metze +1
This paper proposes a novel approach to create an unit set for CTC based speech recognition systems. By using Byte Pair Encoding we learn an unit set of an arbitrary size on a give…
Visual Features for Context-Aware Speech Recognition
Abhinav Gupta, Yajie Miao, Leonardo Neves +1
Automatic transcriptions of consumer-generated multi-media content such as "Youtube" videos still exhibit high word error rates. Such data typically occupies a very broad domain, h…
Comparison of Decoding Strategies for CTC Acoustic Models
Thomas Zenkel, Ramon Sanabria, Florian Metze +4
Connectionist Temporal Classification has recently attracted a lot of interest as it offers an elegant approach to building acoustic models (AMs) for speech recognition. The CTC lo…
A Comparison of deep learning methods for environmental sound
Juncheng Li, Wei Dai, Florian Metze +2
Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically a…