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.
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.
We compare features for dynamic time warping (DTW) when used to bootstrap keyword spotting (KWS) in an almost zero-resource setting.
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.
We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting.
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.