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2020 – today
- 2024
[c45]Joshua Miles Jansen van Vüren, Febe de Wet, Thomas Niesler:
Automatic Partitioning of a Code-Switched Speech Corpus Using Mixed-Integer Programming. LREC/COLING 2024: 1944-1952- 2023
- [j10]Rynhardt Kruger
, Febe de Wet, Thomas Niesler:
Mathematical Content Browsing for Print-disabled Readers Based on Virtual-world Exploration and Audio-visual Sensory Substitution. ACM Trans. Access. Comput. 16(2): 12:1-12:27 (2023) - 2022
- [j9]Astik Biswas, Emre Yilmaz, Ewald van der Westhuizen, Febe de Wet, Thomas Niesler:
Code-switched automatic speech recognition in five South African languages. Comput. Speech Lang. 71: 101262 (2022) - [i7]Rynhardt Kruger, Febe de Wet, Thomas Niesler:
Mathematical Content Browsing for Print-Disabled Readers Based on Virtual-World Exploration and Audio-Visual Sensory substitution. CoRR abs/2202.01639 (2022) - 2020
- [c44]Astik Biswas, Febe de Wet, Ewald van der Westhuizen, Thomas Niesler:
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition. CodeSwitch@LREC 2020: 52-56 - [c43]Rynhardt Kruger, Febe de Wet, Thomas Niesler:
Interactive Image Exploration for Visually Impaired Readers using Audio-augmented Touch Gestures. IV 2020: 544-549 - [c42]Astik Biswas, Emre Yilmaz, Febe de Wet, Ewald van der Westhuizen, Thomas Niesler:
Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages. LREC 2020: 3468-3474 - [c41]Nick Wilkinson, Astik Biswas, Emre Yilmaz, Febe de Wet, Ewald van der Westhuizen, Thomas Niesler:
Semi-supervised Acoustic Modelling for Five-lingual Code-switched ASR using Automatically-segmented Soap Opera Speech. SLTU-CCURL@LREC 2020: 70-78 - [i6]Astik Biswas, Emre Yilmaz, Febe de Wet, Ewald van der Westhuizen, Thomas Niesler:
Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages. CoRR abs/2003.03135 (2020) - [i5]Astik Biswas, Febe de Wet, Ewald van der Westhuizen, Thomas R. Niesler:
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition. CoRR abs/2004.04054 (2020) - [i4]Nick Wilkinson, Astik Biswas, Emre Yilmaz, Febe de Wet, Ewald van der Westhuizen, Thomas R. Niesler:
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech. CoRR abs/2004.06480 (2020) - [i3]Trideba Padhi, Astik Biswas, Febe de Wet, Ewald van der Westhuizen, Thomas Niesler:
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages. CoRR abs/2011.03118 (2020)
2010 – 2019
- 2019
- [j8]Jaco Badenhorst, Febe de Wet:
The Usefulness of Imperfect Speech Data for ASR Development in Low-Resource Languages. Inf. 10(9): 268 (2019) - [c40]Astik Biswas, Emre Yilmaz, Febe de Wet, Ewald van der Westhuizen, Thomas Niesler:
Semi-Supervised Acoustic Model Training for Five-Lingual Code-Switched ASR. INTERSPEECH 2019: 3745-3749 - [i2]Astik Biswas, Emre Yilmaz, Febe de Wet, Ewald van der Westhuizen, Thomas Niesler:
Semi-supervised acoustic model training for five-lingual code-switched ASR. CoRR abs/1906.08647 (2019) - 2018
- [c39]Emre Yilmaz, Astik Biswas, Ewald van der Westhuizen, Febe de Wet, Thomas Niesler:
Building a Unified Code-Switching ASR System for South African Languages. INTERSPEECH 2018: 1923-1927 - [c38]Astik Biswas, Febe de Wet, Ewald van der Westhuizen, Emre Yilmaz, Thomas Niesler:
Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech. INTERSPEECH 2018: 2603-2607 - [c37]Astik Biswas, Ewald van der Westhuizen, Thomas Niesler, Febe de Wet:
Improving ASR for Code-Switched Speech in Under-Resourced Languages Using Out-of-Domain Data. SLTU 2018: 122-126 - [i1]Emre Yilmaz, Astik Biswas, Ewald van der Westhuizen, Febe de Wet, Thomas Niesler:
Building a Unified Code-Switching ASR System for South African Languages. CoRR abs/1807.10949 (2018) - 2016
- [c36]Ilana Wilken, Febe de Wet, Elsabé Taljard:
Mobile Assisted Language Learning Applications for Health Care Sciences Students: A User Experience Study. HCI (27) 2016: 287-292 - [c35]Febe de Wet, Jaco Badenhorst, Thipe Modipa:
Developing Speech Resources from Parliamentary Data for South African English. SLTU 2016: 45-52 - 2015
- [c34]Reza Sahraeian, Dirk Van Compernolle, Febe de Wet:
Under-resourced speech recognition based on the speech manifold. INTERSPEECH 2015: 1255-1259 - [c33]Avashna Govender, Febe de Wet, Jules-Raymond Tapamo:
HMM adaptation for child speech synthesis. INTERSPEECH 2015: 1640-1644 - [c32]Ilana Wilken, Febe de Wet, Elsabé Taljard:
A mobile vocabulary acquisition application for health science students: a proposed study. LTLT@SLaTE 2015: 20-24 - [c31]Helmer Strik, Luigi Palumbo, Febe de Wet, Catia Cucchiarini:
Web-based mini-games for language learning that support spoken interaction. SLaTE 2015: 137-142 - [c30]Febe de Wet, Laurette Marais, Daleen Klop:
Text-to-speech enhanced ebooks for emerging literacy development. SLaTE 2015: 173-177 - 2014
- [j7]Herman Kamper, Febe de Wet, Thomas Hain, Thomas Niesler:
Capitalising on North American speech resources for the development of a South African English large vocabulary speech recognition system. Comput. Speech Lang. 28(6): 1255-1268 (2014) - [j6]Nic J. de Vries, Marelie H. Davel, Jaco Badenhorst, Willem D. Basson, Febe de Wet, Etienne Barnard, Alta de Waal:
A smartphone-based ASR data collection tool for under-resourced languages. Speech Commun. 56: 119-131 (2014) - [c29]Reza Sahraeian, Dirk Van Compernolle, Febe de Wet:
On using intrinsic spectral analysis for low-resource languages. SLTU 2014: 61-65 - [c28]Etienne Barnard, Marelie H. Davel, Charl Johannes van Heerden, Febe de Wet, Jaco Badenhorst:
The NCHLT speech corpus of the South African languages. SLTU 2014: 194-200 - [c27]Raymond Molapo, Etienne Barnard, Febe de Wet:
Speech data collection in an under-resourced language within a multilingual context. SLTU 2014: 238-242 - 2013
- [c26]Johannes A. Louw, Georg I. Schlünz, Willem van der Walt, Febe de Wet, Laurette Pretorius:
The Speect text-to-speech system entry for the Blizzard Challenge 2013. Blizzard Challenge 2013 - [c25]Nic J. de Vries, Febe de Wet:
Off-line mobile-assisted vocabulary training for the developing world. SLaTE 2013: 102-103 - 2012
- [c24]Herman Kamper, Febe de Wet, Thomas Hain, Thomas Niesler:
Resource development and experiments in automatic south african broadcast news transcription. SLTU 2012: 102-106 - [c23]Jaco Badenhorst, Alta de Waal, Febe de Wet:
Quality measurements for mobile data collection in the developing world. SLTU 2012: 139-145 - 2011
- [c22]Febe de Wet, Alta de Waal, Gerhard B. Van Huyssteen:
Developing a Broadband Automatic Speech Recognition System for Afrikaans. INTERSPEECH 2011: 3185-3188 - [c21]Febe de Wet, Pieter Müller, Christa van der Walt, Thomas Niesler:
Readability index as a design criterion for elicited imitation tasks in automatic oral proficiency assessment. SLaTE 2011: 105-108 - 2010
- [c20]Marelie H. Davel, Febe de Wet:
Verifying pronunciation dictionaries using conflict analysis. INTERSPEECH 2010: 1898-1901 - [c19]Thipe Modipa, Marelie H. Davel, Febe de Wet:
Acoustic modelling of Sepedi affricates for ASR. SAICSIT 2010: 394-398
2000 – 2009
- 2009
- [j5]Thomas Niesler, Febe de Wet:
The effect of code-mixing on accent identification accuracy. Comput. Speech Lang. 23(4): 435-443 (2009) - [j4]Helmer Strik, Khiet P. Truong, Febe de Wet, Catia Cucchiarini:
Comparing different approaches for automatic pronunciation error detection. Speech Commun. 51(10): 845-852 (2009) - [j3]Febe de Wet, Christa van der Walt, Thomas Niesler:
Automatic assessment of oral language proficiency and listening comprehension. Speech Commun. 51(10): 864-874 (2009) - [c18]Pieter Müller, Febe de Wet, Christa van der Walt, Thomas Niesler:
Automatically assessing the oral proficiency of proficient L2 speakers. SLaTE 2009: 29-32 - 2008
- [c17]Thomas Niesler, Febe de Wet:
Accent identification in the presence of code-mixing. Odyssey 2008: 27 - 2007
- [c16]Febe de Wet, Christa van der Walt, Thomas Niesler:
Automatic large-scale oral language proficiency assessment. INTERSPEECH 2007: 218-221 - [c15]Helmer Strik, Khiet P. Truong, Febe de Wet, Catia Cucchiarini:
Comparing classifiers for pronunciation error detection. INTERSPEECH 2007: 1837-1840 - [c14]Catia Cucchiarini, Ambra Neri, Febe de Wet, Helmer Strik:
ASR-based pronunciation training: scoring accuracy and pedagogical effectiveness of a system for dutch L2 learners. INTERSPEECH 2007: 2181-2184 - 2005
- [j2]Febe de Wet, Johan de Veth, Lou Boves, Bert Cranen:
Additive background noise as a source of non-linear mismatch in the cepstral and log-energy domain. Comput. Speech Lang. 19(1): 31-54 (2005) - [c13]Khiet P. Truong, Ambra Neri, Febe de Wet, Catia Cucchiarini, Helmer Strik:
Automatic detection of frequent pronunciation errors made by L2-learners. INTERSPEECH 2005: 1345-1348 - 2004
- [c12]Eric Sanders, Febe de Wet:
Histogram normalisation and the recognition of names and ontology words in the MUMIS project. INTERSPEECH 2004: 1605-1608 - 2003
- [c11]Febe de Wet, Johan de Veth, Bert Cranen, Louis Boves:
The impact of spectral and energy mismatch on the Aurora2 digit recognition task. ICASSP (2) 2003: 105-108 - [c10]Janienke Sturm, Judith M. Kessens, Mirjam Wester, Febe de Wet, Eric Sanders, Helmer Strik:
Automatic transcription of football commentaries in the MUMIS project. INTERSPEECH 2003: 1853-1856 - 2002
- [c9]Febe de Wet, Johan de Veth, Bert Cranen, Lou Boves:
Accumulated kullback divergence for analysis of ASR performance in the presence of noise. INTERSPEECH 2002: 1069-1072 - [c8]Katrin Weber, Febe de Wet, Bert Cranen, Lou Boves, Samy Bengio, Hervé Bourlard:
Evaluation of formant-like features for ASR. INTERSPEECH 2002: 2101-2104 - 2001
- [j1]Johan de Veth, Febe de Wet, Bert Cranen, Lou Boves:
Acoustic features and a distance measure that reduce the impact of training-test mismatch in ASR. Speech Commun. 34(1-2): 57-74 (2001) - [c7]Johan de Veth, Laurent Mauuary, Bernhard Noé, Febe de Wet, Jürgen Sienel, Lou Boves, Denis Jouvet:
Feature vector selection to improve ASR robustness in noisy conditions. INTERSPEECH 2001: 201-204 - [c6]Bernhard Noé, Jürgen Sienel, Denis Jouvet, Laurent Mauuary, Johan de Veth, Lou Boves, Febe de Wet:
Noise reduction for noise robust feature extraction for distributed speech recognition. INTERSPEECH 2001: 433-436 - [c5]Febe de Wet, Bert Cranen, Johan de Veth, Lou Boves:
A comparison of LPC and FFT-based acoustic features for noise robust ASR. INTERSPEECH 2001: 865-868 - 2000
- [c4]Febe de Wet, Bert Cranen, Johan de Veth, Louis Boves:
Comparing acoustic features for robust ASR in fixed and cellular network applications. ICASSP 2000: 1415-1418
1990 – 1999
- 1999
- [c3]Johan de Veth, Bert Cranen, Febe de Wet, Lou Boves:
Acoustic pre-processing for optimal effectivity of missing feature theory. EUROSPEECH 1999: 65-68 - [c2]Febe de Wet, Catia Cucchiarini, Helmer Strik, Lou Boves:
Using likelihood ratios to perform utterance verification in automatic pronunciation assessment. EUROSPEECH 1999: 173-176 - 1998
- [c1]Catia Cucchiarini, Febe de Wet, Helmer Strik, Lou Boves:
Assessment of dutch pronunciation by means of automatic speech recognition technology. ICSLP 1998
Coauthor Index
aka: Thomas R. Niesler
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last updated on 2024-09-26 23:40 CEST by the dblp team
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