21 citations · 45 across the 4 of their papers we have counts for
7 papers
Text-Driven Separation of Arbitrary Sounds
Kevin Kilgour, Beat Gfeller, Qingqing Huang +3
We propose a method of separating a desired sound source from a single-channel mixture, based on either a textual description or a short audio sample of the target source. This is…
Teaching keyword spotters to spot new keywords with limited examples
Abhijeet Awasthi, Kevin Kilgour, Hassan Rom
Learning to recognize new keywords with just a few examples is essential for personalizing keyword spotting (KWS) models to a user's choice of keywords. However, modern KWS models…
Low Latency ASR for Simultaneous Speech Translation
Thai Son Nguyen, Jan Niehues, Eunah Cho +6
User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal. We therefore have worked on several techniques f…
Training Keyword Spotters with Limited and Synthesized Speech Data
James Lin, Kevin Kilgour, Dominik Roblek +1
With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tas…
Fréchet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms
Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek +1
We propose the Fréchet Audio Distance (FAD), a novel, reference-free evaluation metric for music enhancement algorithms. We demonstrate how typical evaluation metrics for speech en…
Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition
David B. Ramsay, Kevin Kilgour, Dominik Roblek +1
Low power digital signal processors (DSPs) typically have a very limited amount of memory in which to cache data. In this paper we develop efficient bottleneck feature (BNF) extrac…