| Comparing different approaches for automatic pronunciation error detection H Strik, K Truong, F de Wet, C Cucchiarini Speech Communication 51 (10), 845-852, 2009 | 167 | 2009 |
| A smartphone-based ASR data collection tool for under-resourced languages NJ De Vries, MH Davel, J Badenhorst, WD Basson, F de Wet, E Barnard, ... Speech Communication 56, 119-131, 2014 | 128 | 2014 |
| The NCHLT speech corpus of the South African languages E Barnard, MH Davel, C van Heerden, F de Wet, J Badenhorst Proccedings of the 4th Workshop on Spoken Language Technologies for Under …, 2014 | 86 | 2014 |
| Implications of Sepedi/English code switching for ASR systems TI Modipa, F de Wet, MH Davel Proceedings of PRASA, 2013 | 52 | 2013 |
| Automatic assessment of oral language proficiency and listening comprehension F de Wet, C Van der Walt, TR Niesler Speech Communication 51 (10), 864-874, 2009 | 49 | 2009 |
| Comparing classifiers for pronunciation error detection H Strik, KP Truong, F de Wet, C Cucchiarini Interspeech, Antwerp, Belgium, 2007 | 48 | 2007 |
| Assessment of Dutch pronunciation by means of automatic speech recognition technology C Cucchiarini, F de Wet, H Strik, LWJ Boves ICSLP, Sydney, Australia, 1998 | 36 | 1998 |
| Evaluation of formant-like features on an automatic vowel classification task F de Wet, K Weber, L Boves, B Cranen, S Bengio, H Bourlard The Journal of the Acoustical Society of America 116 (3), 1781-1792, 2004 | 34* | 2004 |
| Missing feature theory in ASR: make sure you miss the right type of features JM de Veth, F de Wet, B Cranen, LWJ Boves NOKIA Workshop on Robust Methods for Speech Recognition in Adverse …, 1999 | 28 | 1999 |
| Automatic detection of frequent pronunciation errors made by L2-learners KP Truong, A Neri, F de Wet, C Cucchiarini, H Strik Interspeech, Lisbon, Portugal, 2005 | 27 | 2005 |
| Verifying pronunciation dictionaries using conflict analysis. MH Davel, F de Wet Interspeech, Makuhari, Chiba, Japan, 1898-1901, 2010 | 23 | 2010 |
| Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech. A Biswas, F de Wet, E van der Westhuizen, E Yilmaz, T Niesler Interspeech, Hyderabad, India, 2603-2607, 2018 | 21 | 2018 |
| Quality measurements for mobile data collection in the developing world J Badenhorst, A de Waal, F de Wet Proccedings of the 3rd Workshop on Spoken Language Technologies for Under …, 2012 | 19 | 2012 |
| Additive background noise as a source of non-linear mismatch in the cepstral and log-energy domain F de Wet, J de Veth, L Boves, B Cranen Computer Speech & Language 19 (1), 31-54, 2005 | 19 | 2005 |
| Automatically assessing the oral proficiency of proficient L2 speakers P Müller, F de Wet, C van der Walt, T Niesler International Workshop on Speech and Language Technology in Education, 2009 | 18 | 2009 |
| The origin of Afrikaans pronunciation: a comparison to west Germanic languages and Dutch dialects W Heeringa, F de Wet Proceedings of PRASA, 159-164, 2008 | 18 | 2008 |
| Acoustic features and a distance measure that reduce the impact of training–test mismatch in ASR J De Veth, F de Wet, B Cranen, L Boves Speech Communication 34 (1-2), 57-74, 2001 | 18 | 2001 |
| Noise reduction for noise robust feature extraction for distributed speech recognition B Noé, J Sienel, D Jouvet, L Mauuary, LWJ Boves, JM de Veth, F de Wet Interspeech, Aalborg, Denmark, 2001 | 18 | 2001 |
| Feature vector selection to improve ASR robustness in noisy conditions J de Veth, L Mauuary, B Noe, F de Wet, J Sienel, L Boves, D Jouvet Interspeech, Aalborg, Denmark, 2001 | 17 | 2001 |
| Capitalising on North American speech resources for the development of a South African English large vocabulary speech recognition system H Kamper, F de Wet, T Hain, T Niesler Computer Speech & Language 28 (6), 1255-1268, 2014 | 16 | 2014 |
Thomas NieslerDepartment of Electrical and Electronic Engineering, University of Stellenbosch, South AfricaVerified email at sun.ac.za
