no code implementations • 17 Oct 2023 • Wojciech Sirko, Emmanuel Asiedu Brempong, Juliana T. C. Marcos, Abigail Annkah, Abel Korme, Mohammed Alewi Hassen, Krishna Sapkota, Tomer Shekel, Abdoulaye Diack, Sella Nevo, Jason Hickey, John Quinn
Mapping buildings and roads automatically with remote sensing typically requires high-resolution imagery, which is expensive to obtain and often sparsely available.
no code implementations • 13 Aug 2021 • Ewald van der Westhuizen, Herman Kamper, Raghav Menon, John Quinn, Thomas Niesler
We show that, using these features, the CNN-DTW keyword spotter performs almost as well as the DTW keyword spotter while outperforming a baseline CNN trained only on the keyword templates.
no code implementations • 26 Jul 2021 • Wojciech Sirko, Sergii Kashubin, Marvin Ritter, Abigail Annkah, Yasser Salah Eddine Bouchareb, Yann Dauphin, Daniel Keysers, Maxim Neumann, Moustapha Cisse, John Quinn
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes.
no code implementations • 14 Nov 2018 • Raghav Menon, Herman Kamper, Ewald van der Westhuizen, John Quinn, Thomas Niesler
We compare features for dynamic time warping (DTW) when used to bootstrap keyword spotting (KWS) in an almost zero-resource setting.
no code implementations • 23 Jul 2018 • Raghav Menon, Astik Biswas, Armin Saeb, John Quinn, Thomas Niesler
We present our first efforts in building an automatic speech recognition system for Somali, an under-resourced language, using 1. 57 hrs of annotated speech for acoustic model training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
no code implementations • 23 Jul 2018 • Raghav Menon, Herman Kamper, Emre Yilmaz, John Quinn, Thomas Niesler
We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting.
no code implementations • 25 Jun 2018 • Raghav Menon, Herman Kamper, John Quinn, Thomas Niesler
While the resulting CNN keyword spotter cannot match the performance of the DTW-based system, it substantially outperforms a CNN classifier trained only on the keywords, improving the area under the ROC curve from 0. 54 to 0. 64.