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ORCIDHugo Van hamme
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- affiliation: KU Leuven, Center for Processing Speech and Images
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2020 – today
- 2024
[j49]Yihao Li
, Meng Sun, Xiongwei Zhang, Hugo Van hamme:
Scale-aware dual-branch complex convolutional recurrent network for monaural speech enhancement. Comput. Speech Lang. 86: 101618 (2024)- [j48]Bernd Accou, Lies Bollens, Marlies Gillis, Wendy Verheijen, Hugo Van hamme, Tom Francart:
SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants. Data 9(8): 94 (2024) - [c186]Jakob Poncelet, Hugo Van hamme:
Unsupervised Accent Adaptation Through Masked Language Model Correction of Discrete Self-Supervised Speech Units. ICASSP 2024: 10236-10240 - [i44]Pieter De Clercq, Corentin Puffay, Jill Kries, Hugo Van hamme, Maaike Vandermosten, Tom Francart, Jonas Vanthornhout:
Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework. CoRR abs/2401.10291 (2024) - [i43]Quentin Meeus, Marie-Francine Moens, Hugo Van hamme:
MSNER: A Multilingual Speech Dataset for Named Entity Recognition. CoRR abs/2405.11519 (2024) - [i42]Jakob Poncelet, Yujun Wang, Hugo Van hamme:
Efficient Extraction of Noise-Robust Discrete Units from Self-Supervised Speech Models. CoRR abs/2409.02565 (2024) - 2023
- [j47]Pu Wang, Hugo Van hamme:
Benefits of pre-trained mono- and cross-lingual speech representations for spoken language understanding of Dutch dysarthric speech. EURASIP J. Audio Speech Music. Process. 2023(1): 15 (2023) - [c185]Quentin Meeus, Marie-Francine Moens, Hugo Van hamme:
Whisper-Slu: Extending a Pretrained Speech-to-Text Transformer for Low Resource Spoken Language Understanding. ASRU 2023: 1-6 - [c184]Lies Bollens, Mohammad Jalilpour-Monesi, Bernd Accou, Jonas Vanthornhout, Hugo Van hamme, Tom Francart:
ICASSP 2023 Auditory EEG Decoding Challenge. ICASSP 2023: 1-2 - [c183]Steven Vander Eeckt, Hugo Van hamme:
Weight Averaging: A Simple Yet Effective Method to Overcome Catastrophic Forgetting in Automatic Speech Recognition. ICASSP 2023: 1-5 - [c182]Steven Vander Eeckt, Hugo Van hamme:
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech Recognition. ICASSP 2023: 1-5 - [c181]Bastiaan Tamm, Rik Vandenberghe, Hugo Van hamme:
Cross-Lingual Transfer Learning for Alzheimer's Detection from Spontaneous Speech. ICASSP 2023: 1-2 - [c180]Jinzi Qi, Hugo Van hamme:
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation. INTERSPEECH 2023: 151-155 - [c179]Steven Vander Eeckt, Hugo Van hamme:
Rehearsal-Free Online Continual Learning for Automatic Speech Recognition. INTERSPEECH 2023: 944-948 - [c178]Bastiaan Tamm, Rik Vandenberghe, Hugo Van hamme:
Analysis of XLS-R for Speech Quality Assessment. WASPAA 2023: 1-5 - [i41]Corentin Puffay, Bernd Accou, Lies Bollens, Mohammad Jalilpour-Monesi, Jonas Vanthornhout, Hugo Van hamme, Tom Francart:
Relating EEG to continuous speech using deep neural networks: a review. CoRR abs/2302.01736 (2023) - [i40]Bastiaan Tamm, Rik Vandenberghe, Hugo Van hamme:
Cross-Lingual Transfer Learning for Alzheimer's Detection From Spontaneous Speech. CoRR abs/2303.03049 (2023) - [i39]Jinzi Qi, Hugo Van hamme:
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation. CoRR abs/2306.07090 (2023) - [i38]Steven Vander Eeckt, Hugo Van hamme:
Rehearsal-Free Online Continual Learning for Automatic Speech Recognition. CoRR abs/2306.10860 (2023) - [i37]Bastiaan Tamm, Rik Vandenberghe, Hugo Van hamme:
Analysis of XLS-R for Speech Quality Assessment. CoRR abs/2308.12077 (2023) - [i36]Jakob Poncelet, Hugo Van hamme:
Unsupervised Accent Adaptation Through Masked Language Model Correction Of Discrete Self-Supervised Speech Units. CoRR abs/2309.13994 (2023) - 2022
- [j46]Wim Boes, Hugo Van hamme:
Multi-encoder attention-based architectures for sound recognition with partial visual assistance. EURASIP J. Audio Speech Music. Process. 2022(1): 25 (2022) - [c177]Dávid Sztahó, Miklós Gábriel Tulics, Jinzi Qi, Hugo Van hamme, Klára Vicsi:
Cross-lingual Detection of Dysphonic Speech for Dutch and Hungarian Datasets. BIOSIGNALS 2022: 215-220 - [c176]Wim Boes, Hugo Van hamme:
Impact of Temporal Resolution on Convolutional Recurrent Networks for Audio Tagging and Sound Event Detection. DCASE 2022 - [c175]Steven Vander Eeckt, Hugo Van hamme:
Continual Learning for Monolingual End-to-End Automatic Speech Recognition. EUSIPCO 2022: 459-463 - [c174]Lies Bollens, Tom Francart, Hugo Van hamme:
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders. ICASSP 2022: 1256-1260 - [c173]Pu Wang, Hugo Van hamme:
Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding. INTERSPEECH 2022: 1248-1252 - [c172]Corentin Puffay, Jana Van Canneyt, Jonas Vanthornhout, Hugo Van hamme, Tom Francart:
Relating the fundamental frequency of speech with EEG using a dilated convolutional network. INTERSPEECH 2022: 4038-4042 - [c171]Quentin Meeus, Marie-Francine Moens, Hugo Van hamme:
Multitask Learning for Low Resource Spoken Language Understanding. INTERSPEECH 2022: 4073-4077 - [c170]Bastiaan Tamm, Helena Balabin, Rik Vandenberghe, Hugo Van hamme:
Pre-trained Speech Representations as Feature Extractors for Speech Quality Assessment in Online Conferencing Applications. INTERSPEECH 2022: 4083-4087 - [c169]Wei Liu, Meng Sun, Xiongwei Zhang, Hugo Van hamme, Thomas Fang Zheng:
A Multi-Resolution Front-End for End-to-End Speech Anti-Spoofing. Odyssey 2022: 120-125 - [c168]Jakob Poncelet, Hugo Van hamme:
Learning to Jointly Transcribe and Subtitle for End-To-End Spontaneous Speech Recognition. SLT 2022: 182-189 - [c167]Jinzi Qi, Hugo Van hamme:
Weak-Supervised Dysarthria-Invariant Features for Spoken Language Understanding Using an Fhvae and Adversarial Training. SLT 2022: 375-381 - [i35]Pu Wang, Hugo Van hamme:
Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding. CoRR abs/2206.14318 (2022) - [i34]Lies Bollens, Tom Francart, Hugo Van hamme:
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders. CoRR abs/2207.00323 (2022) - [i33]Wim Boes, Hugo Van hamme:
Multi-encoder attention-based architectures for sound recognition with partial visual assistance. CoRR abs/2209.12826 (2022) - [i32]Wim Boes, Hugo Van hamme:
Impact of temporal resolution on convolutional recurrent networks for audio tagging and sound event detection. CoRR abs/2209.12843 (2022) - [i31]Bastiaan Tamm, Helena Balabin, Rik Vandenberghe, Hugo Van hamme:
Pre-trained Speech Representations as Feature Extractors for Speech Quality Assessment in Online Conferencing Applications. CoRR abs/2210.00259 (2022) - [i30]Jakob Poncelet, Hugo Van hamme:
Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognition. CoRR abs/2210.07771 (2022) - [i29]Wim Boes, Hugo Van hamme:
Optimizing Temporal Resolution Of Convolutional Recurrent Neural Networks For Sound Event Detection. CoRR abs/2210.10208 (2022) - [i28]Wim Boes, Hugo Van hamme:
Multi-Source Transformer Architectures for Audiovisual Scene Classification. CoRR abs/2210.10212 (2022) - [i27]Jinzi Qi, Hugo Van hamme:
Weak-Supervised Dysarthria-invariant Features for Spoken Language Understanding using an FHVAE and Adversarial Training. CoRR abs/2210.13144 (2022) - [i26]Steven Vander Eeckt, Hugo Van hamme:
Weight Averaging: A Simple Yet Effective Method to Overcome Catastrophic Forgetting in Automatic Speech Recognition. CoRR abs/2210.15282 (2022) - [i25]Wim Boes, Hugo Van hamme:
Impact of visual assistance for automated audio captioning. CoRR abs/2211.10539 (2022) - [i24]Quentin Meeus, Marie-Francine Moens, Hugo Van hamme:
Multitask Learning for Low Resource Spoken Language Understanding. CoRR abs/2211.13703 (2022) - [i23]Quentin Meeus, Marie-Francine Moens, Hugo Van hamme:
Bidirectional Representations for Low Resource Spoken Language Understanding. CoRR abs/2211.14320 (2022) - 2021
- [j45]Jakob Poncelet, Vincent Renkens, Hugo Van hamme:
Low resource end-to-end spoken language understanding with capsule networks. Comput. Speech Lang. 66: 101142 (2021) - [j44]Timothy Callemein, Tom Roussel, Ali Diba, Floris De Feyter, Wim Boes, Luc Van Eycken, Luc Van Gool, Hugo Van hamme, Tinne Tuytelaars, Toon Goedemé:
Show me where the action is! Multim. Tools Appl. 80(1): 383-408 (2021) - [c166]Jakob Poncelet, Hugo Van hamme:
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch. ASRU 2021: 169-176 - [c165]Pu Wang, Bagher BabaAli, Hugo Van hamme:
A Study into Pre-Training Strategies for Spoken Language Understanding on Dysarthric Speech. Interspeech 2021: 36-40 - [c164]Mohammad Jalilpour-Monesi, Bernd Accou, Tom Francart, Hugo Van hamme:
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model. Interspeech 2021: 526-530 - [c163]Jinzi Qi, Hugo Van hamme:
Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-Encoders. Interspeech 2021: 1917-1921 - [c162]Wim Boes, Hugo Van hamme:
Audiovisual Transfer Learning for Audio Tagging and Sound Event Detection. Interspeech 2021: 2401-2405 - [c161]Pu Wang, Hugo Van hamme:
A Light Transformer For Speech-To-Intent Applications. SLT 2021: 997-1003 - [c160]Aku Rouhe, Astrid Van Camp, Mittul Singh, Hugo Van hamme, Mikko Kurimo:
An Equal Data Setting for Attention-Based Encoder-Decoder and HMM/DNN Models: A Case Study in Finnish ASR. SPECOM 2021: 602-613 - [i22]Pu Wang, Hugo Van hamme:
Pre-training for low resource speech-to-intent applications. CoRR abs/2103.16674 (2021) - [i21]Bernd Accou, Mohammad Jalilpour-Monesi, Hugo Van hamme, Tom Francart:
Predicting speech intelligibility from EEG using a dilated convolutional network. CoRR abs/2105.06844 (2021) - [i20]Wim Boes, Hugo Van hamme:
Audiovisual transfer learning for audio tagging and sound event detection. CoRR abs/2106.05408 (2021) - [i19]Wim Boes, Robbe Van Rompaey, Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq:
On the long-term learning ability of LSTM LMs. CoRR abs/2106.08927 (2021) - [i18]Mohammad Jalilpour-Monesi, Bernd Accou, Tom Francart, Hugo Van hamme:
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model. CoRR abs/2106.09622 (2021) - [i17]Jakob Poncelet, Hugo Van hamme:
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch. CoRR abs/2109.14357 (2021) - [i16]Wei Liu, Meng Sun, Xiongwei Zhang, Hugo Van hamme, Thomas Fang Zheng:
A Multi-Resolution Front-End for End-to-End Speech Anti-Spoofing. CoRR abs/2110.05087 (2021) - [i15]Steven Vander Eeckt, Hugo Van hamme:
Continual Learning for Monolingual End-to-End Automatic Speech Recognition. CoRR abs/2112.09427 (2021) - 2020
- [j43]Lyan Verwimp, Hugo Van hamme, Patrick Wambacq:
State gradients for analyzing memory in LSTM language models. Comput. Speech Lang. 61: 101034 (2020) - [c159]Wim Boes, Robbe Van Rompaey, Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq:
On the long-term learning ability of LSTM LMs. ESANN 2020: 625-630 - [c158]Bernd Accou, Mohammad Jalilpour-Monesi, Jair Montoya-Martínez, Hugo Van hamme, Tom Francart:
Modeling the relationship between acoustic stimulus and EEG with a dilated convolutional neural network. EUSIPCO 2020: 1175-1179 - [c157]Mohammad Jalilpour-Monesi, Bernd Accou, Jair Montoya-Martínez, Tom Francart, Hugo Van hamme:
An LSTM Based Architecture to Relate Speech Stimulus to Eeg. ICASSP 2020: 941-945 - [c156]Jakob Poncelet, Hugo Van hamme:
Multitask Learning with Capsule Networks for Speech-to-Intent Applications. ICASSP 2020: 8494-8498 - [i14]Jeroen Zegers, Hugo Van hamme:
Analysis of memory in LSTM-RNNs for source separation. CoRR abs/2009.00551 (2020)
2010 – 2019
- 2019
- [j42]Yinan Li, Meng Sun, Hugo Van hamme, Xiongwei Zhang, Jibin Yang:
Robust Hierarchical Learning for Non-Negative Matrix Factorization With Outliers. IEEE Access 7: 10546-10558 (2019) - [j41]Sayeh Mirzaei, Hugo Van hamme, Shima Khosravani:
Hyperspectral image classification using Non-negative Tensor Factorization and 3D Convolutional Neural Networks. Signal Process. Image Commun. 76: 178-185 (2019) - [c155]Pieter Appeltans, Jeroen Zegers, Hugo Van hamme:
Practical Applicability of Deep Neural Networks for Overlapping Speaker Separation. INTERSPEECH 2019: 1353-1357 - [c154]Jeroen Zegers, Hugo Van hamme:
CNN-LSTM Models for Multi-Speaker Source Separation Using Bayesian Hyper Parameter Optimization. INTERSPEECH 2019: 4589-4593 - [c153]Wim Boes, Hugo Van hamme:
Audiovisual Transformer Architectures for Large-Scale Classification and Synchronization of Weakly Labeled Audio Events. ACM Multimedia 2019: 1961-1969 - [c152]Juan Sebastian Piedrahita Giraldo, Steven Lauwereins, Komail M. H. Badami, Hugo Van hamme, Marian Verhelst:
18μW SoC for near-microphone Keyword Spotting and Speaker Verification. VLSI Circuits 2019: 52- - [i13]Janneke van de Loo, Jort F. Gemmeke, Guy De Pauw, Bart Ons, Walter Daelemans, Hugo Van hamme:
Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models. CoRR abs/1901.10680 (2019) - [i12]Wim Boes, Hugo Van hamme:
Audiovisual Transformer Architectures for Large-Scale Classification and Synchronization of Weakly Labeled Audio Events. CoRR abs/1912.02615 (2019) - [i11]Jeroen Zegers, Hugo Van hamme:
CNN-LSTM models for Multi-Speaker Source Separation using Bayesian Hyper Parameter Optimization. CoRR abs/1912.09254 (2019) - [i10]Pieter Appeltans, Jeroen Zegers, Hugo Van hamme:
Practical applicability of deep neural networks for overlapping speaker separation. CoRR abs/1912.09261 (2019) - 2018
- [c151]Lyan Verwimp, Hugo Van hamme, Vincent Renkens, Patrick Wambacq:
State Gradients for RNN Memory Analysis. BlackboxNLP@EMNLP 2018: 344-346 - [c150]Jeroen Zegers, Hugo Van hamme:
Multi-Scenario Deep Learning for Multi-Speaker Source Separation. ICASSP 2018: 5379-5383 - [c149]Vincent Renkens, Hugo Van hamme:
Capsule Networks for Low Resource Spoken Language Understanding. INTERSPEECH 2018: 601-605 - [c148]Lyan Verwimp, Hugo Van hamme, Vincent Renkens, Patrick Wambacq:
State Gradients for RNN Memory Analysis. INTERSPEECH 2018: 1467-1471 - [c147]Jeroen Zegers, Hugo Van hamme:
Memory Time Span in LSTMs for Multi-Speaker Source Separation. INTERSPEECH 2018: 1477-1481 - [c146]Lyan Verwimp, Hugo Van hamme, Patrick Wambacq:
TF-LM: TensorFlow-based Language Modeling Toolkit. LREC 2018 - [c145]Dries Hulens, Bram Aerts, Punarjay Chakravarty, Ali Diba, Toon Goedemé, Tom Roussel, Jeroen Zegers, Tinne Tuytelaars, Luc Van Eycken, Luc Van Gool, Hugo Van hamme, Joost Vennekens:
The CAMETRON Lecture Recording System: High Quality Video Recording and Editing with Minimal Human Supervision. MMM (1) 2018: 518-530 - [c144]Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq:
Information-Weighted Neural Cache Language Models for ASR. SLT 2018: 756-762 - [i9]Vincent Renkens, Hugo Van hamme:
Capsule Networks for Low Resource Spoken Language Understanding. CoRR abs/1805.02922 (2018) - [i8]Lyan Verwimp, Hugo Van hamme, Vincent Renkens, Patrick Wambacq:
State Gradients for RNN Memory Analysis. CoRR abs/1805.04264 (2018) - [i7]Jeroen Zegers, Hugo Van hamme:
Multi-scenario deep learning for multi-speaker source separation. CoRR abs/1808.08095 (2018) - [i6]Jeroen Zegers, Hugo Van hamme:
Memory Time Span in LSTMs for Multi-Speaker Source Separation. CoRR abs/1808.08097 (2018) - [i5]Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq:
Information-Weighted Neural Cache Language Models for ASR. CoRR abs/1809.08826 (2018) - 2017
- [j40]Vincent Renkens, Hugo Van hamme:
Automatic relevance determination for nonnegative dictionary learning in the gamma-Poisson model. Signal Process. 132: 121-133 (2017) - [j39]Vincent Renkens, Hugo Van hamme:
Weakly Supervised Learning of Hidden Markov Models for Spoken Language Acquisition. IEEE ACM Trans. Audio Speech Lang. Process. 25(2): 285-295 (2017) - [j38]Deepak Baby, Hugo Van hamme:
Joint Denoising and Dereverberation Using Exemplar-Based Sparse Representations and Decaying Norm Constraint. IEEE ACM Trans. Audio Speech Lang. Process. 25(10): 2024-2035 (2017) - [c143]Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq:
Character-Word LSTM Language Models. EACL (1) 2017: 417-427 - [c142]Jeroen Zegers, Hugo Van hamme:
Improving Source Separation via Multi-Speaker Representations. INTERSPEECH 2017: 1919-1923 - [c141]Amir Hossein Poorjam, Soheila Hesaraki, Saeid Safavi, Hugo Van hamme, Mohamad Hasan Bahari:
Automatic Smoker Detection from Telephone Speech Signals. SPECOM 2017: 200-210 - [i4]Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq:
Character-Word LSTM Language Models. CoRR abs/1704.02813 (2017) - [i3]Jeroen Zegers, Hugo Van hamme:
Improving Source Separation via Multi-Speaker Representations. CoRR abs/1708.08740 (2017) - [i2]Lyan Verwimp, Joris Pelemans, Marieke Lycke, Hugo Van hamme, Patrick Wambacq:
Language Models of Spoken Dutch. CoRR abs/1709.03759 (2017) - 2016
- [j37]Meng Sun, Hugo Van hamme, Yimin Wang, Xiongwei Zhang:
Unsupervised Learning of Continuous Density HMM for Variable-Length Spoken Unit Discovery. IEICE Trans. Inf. Syst. 99-D(1): 296-299 (2016) - [j36]Bert Van Den Broeck, Peter Karsmakers, Hugo Van hamme, Bart Vanrumste:
Noise robust footstep location estimation using a wireless acoustic sensor network. J. Ambient Intell. Smart Environ. 8(6): 665-679 (2016) - [j35]Emre Yilmaz, Jort F. Gemmeke, Hugo Van hamme:
Noise robust exemplar matching with alpha-beta divergence. Speech Commun. 76: 127-142 (2016) - [j34]Sayeh Mirzaei, Hugo Van hamme, Yaser Norouzi:
Under-determined reverberant audio source separation using Bayesian Non-negative Matrix Factorization. Speech Commun. 81: 129-137 (2016) - [j33]Meng Sun, Xiongwei Zhang, Hugo Van hamme, Thomas Fang Zheng:
Unseen Noise Estimation Using Separable Deep Auto Encoder for Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 24(1): 93-104 (2016) - [c140]Louis Onrust, Antal van den Bosch, Hugo Van hamme:
Improving cross-domain n-gram language modelling with skipgrams. ACL (2) 2016 - [c139]Deepak Baby, Hugo Van hamme:
Supervised speech dereverberation in noisy environments using exemplar-based sparse representations. ICASSP 2016: 156-160 - [c138]Emre Yilmaz, Jort F. Gemmeke, Hugo Van hamme:
Data selection for noise robust exemplar matching. ICASSP 2016: 5980-5984 - [c137]Joris Pelemans, Tom Vanallemeersch, Kris Demuynck, Lyan Verwimp, Hugo Van hamme, Patrick Wambacq:
Language model adaptation for ASR of spoken translations using phrase-based translation models and named entity models. ICASSP 2016: 5985-5989 - [c136]Punarjay Chakravarty, Jeroen Zegers, Tinne Tuytelaars, Hugo Van hamme:
Active speaker detection with audio-visual co-training. ICMI 2016: 312-316 - [c135]Jeroen Zegers, Hugo Van hamme:
Joint Sound Source Separation and Speaker Recognition. INTERSPEECH 2016: 2228-2232 - [c134]Joris Pelemans, Lyan Verwimp, Kris Demuynck, Hugo Van hamme, Patrick Wambacq:
SCALE: A Scalable Language Engineering Toolkit. LREC 2016 - [c133]Vincent Renkens, Vikrant Tomar, Hugo Van hamme:
Incrementally learn the relevance of words in a dictionary for spoken language acquisition. SLT 2016: 144-150 - [i1]Jeroen Zegers, Hugo Van hamme:
Joint Sound Source Separation and Speaker Recognition. CoRR abs/1604.08852 (2016) - 2015
- [j32]Sayeh Mirzaei, Yaser Norouzi, Hugo Van hamme:
Two-stage blind audio source counting and separation of stereo instantaneous mixtures using Bayesian tensor factorisation. IET Signal Process. 9(8): 587-595 (2015) - [j31]Meng Sun, Xiongwei Zhang, Hugo Van hamme:
A stable approach for model order selection in nonnegative matrix factorization. Pattern Recognit. Lett. 54: 97-102 (2015) - [j30]Sayeh Mirzaei, Hugo Van hamme, Yaser Norouzi:
Blind audio source counting and separation of anechoic mixtures using the multichannel complex NMF framework. Signal Process. 115: 27-37 (2015) - [j29]Deepak Baby, Tuomas Virtanen, Jort F. Gemmeke, Hugo Van hamme:
Coupled Dictionaries for Exemplar-Based Speech Enhancement and Automatic Speech Recognition. IEEE ACM Trans. Audio Speech Lang. Process. 23(11): 1788-1799 (2015) - [c132]Lode Vuegen, Bert Van Den Broeck, Peter Karsmakers, Hugo Van hamme, Bart Vanrumste:
Monitoring activities of daily living using Wireless Acoustic Sensor Networks in clean and noisy conditions. EMBC 2015: 4966-4969 - [c131]Lode Vuegen, Bert Van Den Broeck, Peter Karsmakers, Hugo Van hamme, Bart Vanrumste:
Energy efficient monitoring of activities of daily living using wireless acoustic sensor networks in clean and noisy conditions. EUSIPCO 2015: 449-453 - [c130]Emre Yilmaz, Deepak Baby, Hugo Van hamme:
Noise robust exemplar matching with coupled dictionaries for single-channel speech enhancement. EUSIPCO 2015: 874-878 - [c129]Deepak Baby, Hugo Van hamme:
Hybrid input spaces for exemplar-based noise robust speech recognition using coupled dictionaries. EUSIPCO 2015: 1676-1680 - [c128]Emre Yilmaz, Hugo Van hamme, Jort F. Gemmeke:
Adaptive noise dictionary design for noise robust exemplar matching of speech. EUSIPCO 2015: 1681-1685 - [c127]Deepak Baby, Jort F. Gemmeke, Tuomas Virtanen, Hugo Van hamme:
Exemplar-based speech enhancement for deep neural network based automatic speech recognition. ICASSP 2015: 4485-4489 - [c126]Joris Pelemans, Kris Demuynck, Hugo Van hamme, Patrick Wambacq:
Improving n-gram probability estimates by compound-head clustering. ICASSP 2015: 5221-5225 - [c125]Punarjay Chakravarty, Sayeh Mirzaei, Tinne Tuytelaars, Hugo Van hamme:
Who's Speaking?: Audio-Supervised Classification of Active Speakers in Video. ICMI 2015: 87-90 - [c124]Emre Yilmaz, Deepak Baby, Hugo Van hamme:
Noise robust exemplar matching for speech enhancement: applications to automatic speech recognition. INTERSPEECH 2015: 688-692 - [c123]Gert Dekkers, Toon van Waterschoot, Bart Vanrumste, Bert Van Den Broeck, Jort F. Gemmeke, Hugo Van hamme, Peter Karsmakers:
A multi-channel speech enhancement framework for robust NMF-based speech recognition for speech-impaired users. INTERSPEECH 2015: 746-750 - [c122]Vincent Renkens, Hugo Van hamme:
Mutually exclusive grounding for weakly supervised non-negative matrix factorisation. INTERSPEECH 2015: 1388-1392 - [c121]Joris Pelemans, Tom Vanallemeersch, Kris Demuynck, Hugo Van hamme, Patrick Wambacq:
Efficient language model adaptation for automatic speech recognition of spoken translations. INTERSPEECH 2015: 2262-2266 - [c120]Deepak Baby, Hugo Van hamme:
Investigating modulation spectrogram features for deep neural network-based automatic speech recognition. INTERSPEECH 2015: 2479-2483 - [c119]Amir Hossein Poorjam, Mohamad Hasan Bahari, Vasileios Vasilakakis, Hugo Van hamme:
Height estimation from speech signals using i-vectors and least-squares support vector regression. TSP 2015: 1-5 - 2014
- [j28]Bart Ons, Jort F. Gemmeke, Hugo Van hamme:
Fast vocabulary acquisition in an NMF-based self-learning vocal user interface. Comput. Speech Lang. 28(4): 997-1017 (2014) - [j27]Mohamad Hasan Bahari, Mitchell McLaren, Hugo Van hamme, David A. van Leeuwen:
Speaker age estimation using i-vectors. Eng. Appl. Artif. Intell. 34: 99-108 (2014) - [j26]Bart Ons, Jort F. Gemmeke, Hugo Van hamme:
The self-taught vocal interface. EURASIP J. Audio Speech Music. Process. 2014: 43 (2014) - [j25]Mohamad Hasan Bahari, Najim Dehak, Hugo Van hamme, Lukás Burget, Ahmed Ali, Jim Glass:
Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition. IEEE ACM Trans. Audio Speech Lang. Process. 22(7): 1117-1129 (2014) - [j24]Emre Yilmaz, Jort Florent Gemmeke, Hugo Van hamme:
Noise Robust Exemplar Matching Using Sparse Representations of Speech. IEEE ACM Trans. Audio Speech Lang. Process. 22(8): 1306-1319 (2014) - [c118]Sayeh Mirzaei, Hugo Van hamme, Yaser Norouzi:
Blind audio source separation of stereo mixtures using Bayesian Non-negative Matrix Factorization. EUSIPCO 2014: 621-625 - [c117]Joris Pelemans, Kris Demuynck, Hugo Van hamme, Patrick Wambacq:
Coping with language data sparsity: Semantic head mapping of compound words. ICASSP 2014: 141-145 - [c116]Deepak Baby, Tuomas Virtanen, Tom Barker, Hugo Van hamme:
Coupled dictionary training for exemplar-based speech enhancement. ICASSP 2014: 2883-2887 - [c115]Tuomas Virtanen, Bhiksha Raj, Jort F. Gemmeke, Hugo Van hamme:
Active-set newton algorithm for non-negative sparse coding of audio. ICASSP 2014: 3092-3096 - [c114]Emre Yilmaz, Jort F. Gemmeke, Hugo Van hamme:
Noise-robust speech recognition with exemplar-based sparse representations using Alpha-Beta divergence. ICASSP 2014: 5502-5506 - [c113]Sayeh Mirzaei, Hugo Van hamme, Yaser Norouzi:
Blind speech source localization, counting and separation for 2-channel convolutive mixtures in a reverberant environment. INTERSPEECH 2014: 860-864 - [c112]Emre Yilmaz, Joris Pelemans, Hugo Van hamme:
Automatic assessment of children's reading with the FLaVoR decoding using a phone confusion model. INTERSPEECH 2014: 969-972 - [c111]Oliver Walter, Vladimir Despotovic, Reinhold Haeb-Umbach, Jort F. Gemmeke, Bart Ons, Hugo Van hamme:
An evaluation of unsupervised acoustic model training for a dysarthric speech interface. INTERSPEECH 2014: 1013-1017 - [c110]Tom Barker, Hugo Van hamme, Tuomas Virtanen:
Modelling primitive streaming of simple tone sequences through factorisation of modulation pattern tensors. INTERSPEECH 2014: 1371-1375 - [c109]Joris Pelemans, Kris Demuynck, Hugo Van hamme, Patrick Wambacq:
Speech Recognition Web Services for Dutch. LREC 2014: 3041-3044 - [c108]Emre Yilmaz, Konstantinos Rematas, Tinne Tuytelaars, Hugo Van hamme:
Learning Like a Toddler: Watching Television Series to Learn Vocabulary from Images and Audio. ACM Multimedia 2014: 1189-1192 - [c107]Najim Dehak, Oldrich Plchot, Mohamad Hasan Bahari, Lukás Burget, Hugo Van hamme, Réda Dehak:
GMM Weights Adaptation Based on Subspace Approaches for Speaker Verification. Odyssey 2014: 48-53 - [c106]Vincent Renkens, Steven Janssens, Bart Ons, Jort F. Gemmeke, Hugo Van hamme:
Acquisition of ordinal words using weakly supervised NMF. SLT 2014: 30-35 - [c105]Jort F. Gemmeke, Siddharth Sehgal, Stuart P. Cunningham, Hugo Van hamme:
Dysarthric vocal interfaces with minimal training data. SLT 2014: 248-253 - [c104]Deepak Baby, Tuomas Virtanen, Jort F. Gemmeke, Tom Barker, Hugo Van hamme:
Exemplar-based noise robust automatic speech recognition using modulation spectrogram features. SLT 2014: 519-524 - 2013
- [j23]Meng Sun, Hugo Van hamme:
Joint training of non-negative Tucker decomposition and discrete density hidden Markov models. Comput. Speech Lang. 27(4): 969-988 (2013) - [j22]Xueru Zhang, Kris Demuynck, Hugo Van hamme:
Rapid speaker adaptation in latent speaker space with non-negative matrix factorization. Speech Commun. 55(9): 893-908 (2013) - [c103]Bart Ons, Jort F. Gemmeke, Hugo Van hamme:
NMF-based keyword learning from scarce data. ASRU 2013: 392-397 - [c102]Emre Yilmaz, Jort F. Gemmeke, Hugo Van hamme:
Exemplar selection techniques for sparse representations of speech using multiple dictionaries. EUSIPCO 2013: 1-5 - [c101]Hugo Van hamme:
A diagonalized newton algorithm for non-negative sparse coding. ICASSP 2013: 7299-7303 - [c100]Mohamad Hasan Bahari, Rahim Saeidi, Hugo Van hamme, David A. van Leeuwen:
Accent recognition using i-vector, Gaussian Mean Supervector and Gaussian posterior probability supervector for spontaneous telephone speech. ICASSP 2013: 7344-7348 - [c99]Emre Yilmaz, Jort F. Gemmeke, Hugo Van hamme:
Embedding time warping in exemplar-based sparse representations of speech. ICASSP 2013: 8076-8080 - [c98]Sayeh Mirzaei, Hugo Van hamme, Yaser Norouzi:
Model order estimation using Bayesian NMF for discovering phone patterns in spoken utterances. INTERSPEECH 2013: 1717-1721 - [c97]Jort F. Gemmeke, Bart Ons, Netsanet M. Tessema, Hugo Van hamme, Janneke van de Loo, Guy De Pauw, Walter Daelemans, Jonathan Huyghe, Jan Derboven, Lode Vuegen, Bert Van Den Broeck, Peter Karsmakers, Bart Vanrumste:
Self-taught assistive vocal interfaces: an overview of the ALADIN project. INTERSPEECH 2013: 2039-2043 - [c96]Lize Broekx, Katrien Dreesen, Jort Florent Gemmeke, Hugo Van hamme:
Comparing and combining classifiers for self-taught vocal interfaces. SLPAT 2013: 21-28 - [c95]Hanne Deprez, Emre Yilmaz, Stefan Lievens, Hugo Van hamme:
Automating speech reception threshold measurements using automatic speech recognition. SLPAT 2013: 35-40 - [c94]Bart Ons, Netsanet M. Tessema, Janneke van de Loo, Jort F. Gemmeke, Guy De Pauw, Walter Daelemans, Hugo Van hamme:
A Self Learning Vocal Interface for Speech-impaired Users. SLPAT 2013: 73-81 - [c93]Lode Vuegen, Bert Van Den Broeck, Peter Karsmakers, Hugo Van hamme, Bart Vanrumste:
Automatic Monitoring of Activities of Daily Living based on Real-life Acoustic Sensor Data: a~preliminary study. SLPAT 2013: 113-118 - [c92]Jort F. Gemmeke, Lode Vuegen, Peter Karsmakers, Bart Vanrumste, Hugo Van hamme:
An exemplar-based NMF approach to audio event detection. WASPAA 2013: 1-4 - [c91]Sayeh Mirzaei, Hugo Van hamme, Yaser Norouzi:
Bayesian non-parametric matrix factorization for discovering words in spoken utterances. WASPAA 2013: 1-4 - [c90]Hugo Van hamme:
The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization. ICLR (Poster) 2013 - [p3]Catia Cucchiarini, Hugo Van hamme:
The JASMIN Speech Corpus: Recordings of Children, Non-natives and Elderly People. Essential Speech and Language Technology for Dutch 2013: 43-59 - [p2]Yujun Wang, Jort F. Gemmeke, Kris Demuynck, Hugo Van hamme:
Missing Data Solutions for Robust Speech Recognition. Essential Speech and Language Technology for Dutch 2013: 289-304 - 2012
- [j21]Yujun Wang, Hugo Van hamme:
Multi-candidate missing data imputation for robust speech recognition. EURASIP J. Audio Speech Music. Process. 2012: 17 (2012) - [j20]Marina B. Ruiter, Lilian Beijer, Catia Cucchiarini, Emiel Krahmer, Toni C. M. Rietveld, Helmer Strik, Hugo Van hamme:
Human language technology and communicative disabilities: requirements and possibilities for the future. Lang. Resour. Evaluation 46(1): 143-151 (2012) - [j19]Joris Driesen, Hugo Van hamme:
Supervised input space scaling for non-negative matrix factorization. Signal Process. 92(8): 1864-1874 (2012) - [j18]Meng Sun, Hugo Van hamme:
Large Scale Graph Regularized Non-Negative Matrix Factorization With ℓ1 Normalization Based on Kullback-Leibler Divergence. IEEE Trans. Signal Process. 60(7): 3876-3880 (2012) - [c89]Janneke van de Loo, Jort F. Gemmeke, Guy De Pauw, Joris Driesen, Hugo Van hamme, Walter Daelemans:
Towards a Self-Learning Assistive Vocal Interface: Vocabulary and Grammar Learning. SMIAE@ACL 2012: 34-42 - [c88]Hugo Van hamme:
An On-Line NMF Model for Temporal Pattern Learning: Theory with Application to Automatic Speech Recognition. LVA/ICA 2012: 306-313 - [c87]Xueru Zhang, Kris Demuynck, Hugo Van hamme:
Latent variable speaker adaptation of Gaussian mixture weights and means. ICASSP 2012: 4349-4352 - [c86]Joris Driesen, Hugo Van hamme:
Fast word acquisition in an NMF-based learning framework. ICASSP 2012: 5137-5140 - [c85]Joris Driesen, Jort F. Gemmeke, Hugo Van hamme:
Weakly supervised keyword learning using sparse representations of speech. ICASSP 2012: 5145-5148 - [c84]Meng Sun, Hugo Van hamme:
Tri-factorization learning of sub-word units with application to vocabulary acquisition. ICASSP 2012: 5177-5180 - [c83]Joris Driesen, Jort F. Gemmeke, Hugo Van hamme:
Data-driven speech representations for NMF-based word learning. SAPA@INTERSPEECH 2012: 98-103 - [c82]Mohamad Hasan Bahari, Mitchell McLaren, Hugo Van hamme, David A. van Leeuwen:
Age Estimation from Telephone Speech using i-vectors. INTERSPEECH 2012: 506-509 - [c81]Emre Yilmaz, Dirk Van Compernolle, Hugo Van hamme:
Robust Tracking for Automatic Reading Tutors. INTERSPEECH 2012: 811-814 - [c80]Jort F. Gemmeke, Janneke van de Loo, Guy De Pauw, Joris Driesen, Hugo Van hamme, Walter Daelemans:
A Self-Learning Assistive Vocal Interface Based on Vocabulary Learning and Grammar Induction. INTERSPEECH 2012: 831-834 - [c79]Jort F. Gemmeke, Hugo Van hamme:
Advances in noise robust digit recognition using hybrid exemplar-based techniques. INTERSPEECH 2012: 2134-2137 - [c78]Mohamad Hasan Bahari, Hugo Van hamme:
Speaker adaptation using Maximum Likelihood General Regression. ISSPA 2012: 29-34 - [c77]Mohamad Hasan Bahari, Hugo Van hamme:
Speaker age estimation using Hidden Markov Model weight supervectors. ISSPA 2012: 517-521 - [c76]Bart Ons, Jort F. Gemmeke, Hugo Van hamme:
Label Noise Robustness and Learning Speed in a Self-Learning Vocal User Interface. IWSDS 2012: 249-259 - [c75]Emre Yilmaz, Dirk Van Compernolle, Hugo Van hamme:
Combining exemplar-based matching and exemplar-based sparse representations of speech. MLSLP 2012: 30-33 - [c74]Emre Yilmaz, Jort F. Gemmeke, Dirk Van Compernolle, Hugo Van hamme:
Noise-robust digit recognition with exemplar-based sparse representations of variable length. MLSP 2012: 1-4 - [c73]Xueru Zhang, Kris Demuynck, Dirk Van Compernolle, Hugo Van hamme:
Subspace-GMM acoustic models for under-resourced languages: feasibility study. SLTU 2012: 1-4 - 2011
- [j17]Joris Driesen, Hugo Van hamme:
Modelling vocabulary acquisition, adaptation and generalization in infants using adaptive Bayesian PLSA. Neurocomputing 74(11): 1874-1882 (2011) - [j16]Peter Karsmakers, Kristiaan Pelckmans, Kris De Brabanter, Hugo Van hamme, Johan A. K. Suykens:
Sparse conjugate directions pursuit with application to fixed-size kernel models. Mach. Learn. 85(1-2): 109-148 (2011) - [j15]Maarten Van Segbroeck, Hugo Van hamme:
Advances in Missing Feature Techniques for Robust Large-Vocabulary Continuous Speech Recognition. IEEE Trans. Speech Audio Process. 19(1): 123-137 (2011) - [c72]Jort F. Gemmeke, Hugo Van hamme:
An hierarchical exemplar-based sparse model of speech, with an application to ASR. ASRU 2011: 101-106 - [c71]Mohamad Hasan Bahari, Hugo Van hamme:
Speaker age estimation and gender detection based on supervised Non-Negative Matrix Factorization. BioMS 2011: 1-6 - [c70]Xueru Zhang, Kris Demuynck, Hugo Van hamme:
Rapid speaker adaptation with speaker adaptive training and non-negative matrix factorization. ICASSP 2011: 4456-4459 - [c69]Kris Demuynck, Dino Seppi, Hugo Van hamme, Dirk Van Compernolle:
Progress in example based automatic speech recognition. ICASSP 2011: 4692-4695 - [c68]Meng Sun, Hugo Van hamme:
Unsupervised vocabulary discovery using non-negative matrix factorization with graph regularization. ICASSP 2011: 5152-5155 - [c67]Hugo Van hamme:
Phonetic analysis of a computational model for vocabulary acquisition from auditory inputs. ICDL-EPIROB 2011: 1-6 - [c66]Meng Sun, Hugo Van hamme:
Image pattern discovery by using the spatial closeness of visual code words. ICIP 2011: 205-208 - [c65]Mohamad Hasan Bahari, Hugo Van hamme:
Rapid Speaker Adaptation using Maximum Likelihood Neural Regression. ICME 2011: 1-6 - [c64]Meng Sun, Hugo Van hamme:
A two-layer non-negative matrix factorization model for vocabulary discovery. MLSLP 2011: 11-15 - [c63]Yujun Wang, Hugo Van hamme:
Gaussian Selection Using Self-Organizing Map for Automatic Speech Recognition. WSOM 2011: 218-227 - [p1]Jort F. Gemmeke, Maarten Van Segbroeck, Yujun Wang, Bert Cranen, Hugo Van hamme:
Automatic Speech Recognition Using Missing Data Techniques: Handling of Real-World Data. Robust Speech Recognition of Uncertain or Missing Data 2011: 157-185 - 2010
- [j14]Jort F. Gemmeke, Hugo Van hamme, Bert Cranen, Louis Boves:
Compressive Sensing for Missing Data Imputation in Noise Robust Speech Recognition. IEEE J. Sel. Top. Signal Process. 4(2): 272-287 (2010) - [c62]Xueru Zhang, Kris Demuynck, Hugo Van hamme:
Histogram equalization and noise masking for robust speech recognition. ICASSP 2010: 4578-4581 - [c61]Kris Demuynck, Xueru Zhang, Dirk Van Compernolle, Hugo Van hamme:
Feature versus model based noise robustness. INTERSPEECH 2010: 721-724 - [c60]Joris Driesen, Hugo Van hamme, W. Bastiaan Kleijn:
Learning from images and speech with Non-negative Matrix Factorization enhanced by input space scaling. SLT 2010: 1-6
2000 – 2009
- 2009
- [j13]Louis ten Bosch, Lou Boves, Hugo Van hamme, Roger K. Moore:
A Computational Model of Language Acquisition: the Emergence of Words. Fundam. Informaticae 90(3): 229-249 (2009) - [j12]Jacques Duchateau, Yuk On Kong, Leen Cleuren, Lukas Latacz, Jan Roelens, Abdurrahman Samir, Kris Demuynck, Pol Ghesquière, Werner Verhelst, Hugo Van hamme:
Developing a reading tutor: Design and evaluation of dedicated speech recognition and synthesis modules. Speech Commun. 51(10): 985-994 (2009) - [j11]Maarten Van Segbroeck, Hugo Van hamme:
Unsupervised learning of time-frequency patches as a noise-robust representation of speech. Speech Commun. 51(11): 1124-1138 (2009) - [j10]Veronique Stouten, Hugo Van hamme:
Automatic voice onset time estimation from reassignment spectra. Speech Commun. 51(12): 1194-1205 (2009) - [c59]Jacques Duchateau, Kris Demuynck, Hugo Van hamme:
Evaluation of phone lattice based speech decoding. INTERSPEECH 2009: 1179-1182 - [c58]Jort F. Gemmeke, Yujun Wang, Maarten Van Segbroeck, Bert Cranen, Hugo Van hamme:
Application of noise robust MDT speech recognition on the SPEECON and speechdat-car databases. INTERSPEECH 2009: 1227-1230 - [c57]Joris Driesen, Louis ten Bosch, Hugo Van hamme:
Adaptive non-negative matrix factorization in a computational model of language acquisition. INTERSPEECH 2009: 1731-1734 - [c56]Maarten Van Segbroeck, Hugo Van hamme:
Applying non-negative matrix factorization on time-frequency reassignment spectra for missing data mask estimation. INTERSPEECH 2009: 2511-2514 - [c55]Louis ten Bosch, Joris Driesen, Hugo Van hamme, Lou Boves:
On a Computational Model for Language Acquisition: Modeling Cross-Speaker Generalisation. TSD 2009: 315-322 - 2008
- [j9]Veronique Stouten, Kris Demuynck, Hugo Van hamme:
Discovering Phone Patterns in Spoken Utterances by Non-Negative Matrix Factorization. IEEE Signal Process. Lett. 15: 131-134 (2008) - [c54]Jacques Duchateau, Tobias Leroy, Kris Demuynck, Hugo Van hamme:
Fast speaker adaptation using non-negative matrix factorization. ICASSP 2008: 4269-4272 - [c53]Maarten Van Segbroeck, Hugo Van hamme:
Robust speech recognition using missing data techniques in the prospect domain and fuzzy masks. ICASSP 2008: 4393-4396 - [c52]Kris Hermus, Laurent Girin, Hugo Van hamme, Sufian Irhimeh:
Estimation of the voicing cut-off frequency contour of natural speech based on harmonic and aperiodic energies. ICASSP 2008: 4473-4476 - [c51]Alexander Bertrand, Kris Demuynck, Veronique Stouten, Hugo Van hamme:
Unsupervised learning of auditory filter banks using non-negative matrix factorisation. ICASSP 2008: 4713-4716 - [c50]Tingyao Wu, Peter Karsmakers, Hugo Van hamme, Dirk Van Compernolle:
Comparison of variable selection methods and classifiers for native accent identification. INTERSPEECH 2008: 305-308 - [c49]Samer Al Moubayed, Michaël De Smet, Hugo Van hamme:
Lip synchronization: from phone lattice to PCA eigen-projections using neural networks. INTERSPEECH 2008: 2016-2019 - [c48]Joris Driesen, Hugo Van hamme:
Improving the multigram algorithm by using lattices as input. INTERSPEECH 2008: 2086-2089 - [c47]Hugo Van hamme:
HAC-models: a novel approach to continuous speech recognition. INTERSPEECH 2008: 2554-2557 - [c46]Louis ten Bosch, Hugo Van hamme, Lou Boves:
A computational model of language acquisition: focus on word discovery. INTERSPEECH 2008: 2570-2573 - [c45]Abdurrahman Samir, Jacques Duchateau, Hugo Van hamme:
Discriminative model combination and language model selection in a reading tutor for children. INTERSPEECH 2008: 2795-2798 - [c44]Leen Cleuren, Jacques Duchateau, Pol Ghesquière, Hugo Van hamme:
Children's Oral Reading Corpus (CHOREC): Description and Assessment of Annotator Agreement. LREC 2008 - [c43]Catia Cucchiarini, Joris Driesen, Hugo Van hamme, Eric Sanders:
Recording Speech of Children, Non-Natives and Elderly People for HLT Applications: the JASMIN-CGN Corpus. LREC 2008 - 2007
- [j8]Kris Hermus, Patrick Wambacq, Hugo Van hamme:
A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition. EURASIP J. Adv. Signal Process. 2007 (2007) - [j7]Kris Hermus, Hugo Van hamme, Sufian Irhimeh:
Estimation of the Voicing Cut-Off Frequency Contour Based on a Cumulative Harmonicity Score. IEEE Signal Process. Lett. 14(11): 820-823 (2007) - [c42]Kris Hermus, Hugo Van hamme, Werner Verhelst, Sufian Irhimeh, Jan De Moortel:
DCT-Based Amplitude and Frequency Modulated Harmonic-Plus-Noise Modelling for Text-to-Speech Synthesis. ICASSP (4) 2007: 685-688 - [c41]Peter Karsmakers, Kristiaan Pelckmans, Johan A. K. Suykens, Hugo Van hamme:
Fixed-size kernel logistic regression for phoneme classification. INTERSPEECH 2007: 78-81 - [c40]Maarten Van Segbroeck, Hugo Van hamme:
Vector-quantization based mask estimation for missing data automatic speech recognition. INTERSPEECH 2007: 910-913 - [c39]Jacques Duchateau, Leen Cleuren, Hugo Van hamme, Pol Ghesquière:
Automatic assessment of children's reading level. INTERSPEECH 2007: 1210-1213 - [c38]Veronique Stouten, Kris Demuynck, Hugo Van hamme:
Automatically learning the units of speech by non-negative matrix factorisation. INTERSPEECH 2007: 1937-1940 - 2006
- [j6]Veronique Stouten, Hugo Van hamme, Patrick Wambacq:
Model-based feature enhancement with uncertainty decoding for noise robust ASR. Speech Commun. 48(11): 1502-1514 (2006) - [c37]Hugo Van hamme:
Handling Time-Derivative Features in a Missing Data Framework for Robust Automatic Speech Recognition. ICASSP (1) 2006: 293-296 - [c36]Tingyao Wu, Dirk Van Compernolle, Jacques Duchateau, Hugo Van hamme:
Maximum Likelihood Based Temporal Frame Selection. ICASSP (1) 2006: 349-352 - [c35]Veronique Stouten, Hugo Van hamme, Patrick Wambacq:
Application of Minimum Statistics and Minima Controlled Recursive Averaging Methods to Estimate a Cepstral Noise Model for Robust ASR. ICASSP (1) 2006: 765-768 - [c34]Leen Cleuren, Jacques Duchateau, Alain Sips, Pol Ghesquière, Hugo Van hamme:
Developing an automatic assessment tool for children²s oral reading. INTERSPEECH 2006 - [c33]Kris Demuynck, Dirk Van Compernolle, Hugo Van hamme:
Robust phone lattice decoding. INTERSPEECH 2006 - [c32]Maarten Van Segbroeck, Hugo Van hamme:
Handling convolutional noise in missing data automatic speech recognition. INTERSPEECH 2006 - [c31]Tingyao Wu, Dirk Van Compernolle, Jacques Duchateau, Hugo Van hamme:
Single frame selection for phoneme classification. INTERSPEECH 2006 - [c30]Catia Cucchiarini, Hugo Van hamme, Olga van Herwijnen, Felix Smits:
JASMIN-CGN: Extension of the Spoken Dutch Corpus with Speech of Elderly People, Children and Non-natives in the Human-Machine Interaction Modality. LREC 2006: 135-138 - 2005
- [c29]Veronique Stouten, Hugo Van hamme, Patrick Wambacq:
Effect of Phase-Sensitive Environment Model and Higher Order VTS on Noisy Speech Feature Enhancement. ICASSP (1) 2005: 433-436 - [c28]Veronique Stouten, Hugo Van hamme, Patrick Wambacq:
Kalman and unscented kalman filter feature enhancement for noise robust ASR. INTERSPEECH 2005: 953-956 - [c27]Jacques Duchateau, Dong Hoon Van Uytsel, Hugo Van hamme, Patrick Wambacq:
Statistical language models for large vocabulary spontaneous speech recognition in dutch. INTERSPEECH 2005: 1301-1304 - [c26]Wim Jansen, Hugo Van hamme:
PROSPECT features and their application to missing data techniques for vocal tract length normalization. INTERSPEECH 2005: 2753-2756 - 2004
- [c25]Hugo Van hamme:
Robust speech recognition using cepstral domain missing data techniques and noisy masks. ICASSP (1) 2004: 213-216 - [c24]Veronique Stouten, Hugo Van hamme, Patrick Wambacq:
Joint removal of additive and convolutional noise with model-based feature enhancement. ICASSP (1) 2004: 949-952 - [c23]Hugo Van hamme:
PROSPECT features and their application to missing data techniques for robust speech recognition. INTERSPEECH 2004: 101-104 - [c22]Hugo Van hamme, Patrick Wambacq, Veronique Stouten:
Accounting for the uncertainty of speech estimates in the context of model-based feature enhancement. INTERSPEECH 2004: 105-108 - [c21]Jacques Duchateau, Tim Ceyssens, Hugo Van hamme:
Use and Evaluation of Prosodic Annotations in Dutch. LREC 2004 - [c20]Tom Laureys, Guy De Pauw, Hugo Van hamme, Walter Daelemans, Dirk Van Compernolle:
Evaluation and Adaptation of the Celex Dutch Morphological Database. LREC 2004 - [c19]Guy De Pauw, Tom Laureys, Walter Daelemans, Hugo Van hamme:
A Comparison of Two Different Approaches to Morphological Analysis of Dutch. SIGMORPHON@ACL 2004 - 2003
- [c18]Veronique Stouten, Hugo Van hamme, Kris Demuynck, Patrick Wambacq:
Robust speech recognition using model-based feature enhancement. INTERSPEECH 2003: 17-20 - [c17]Veronique Stouten, Hugo Van hamme, Jacques Duchateau, Patrick Wambacq:
Evaluation of model-based feature enhancement on the AURORA-4 task. INTERSPEECH 2003: 349-352 - [c16]Kris Demuynck, Tom Laureys, Dirk Van Compernolle, Hugo Van hamme:
FLavor: a flexible architecture for LVCSR. INTERSPEECH 2003: 1973-1976 - [c15]Koen Eneman, Jacques Duchateau, Marc Moonen, Dirk Van Compernolle, Hugo Van hamme:
Assessment of dereverberation algorithms for large vocabulary speech recognition systems. INTERSPEECH 2003: 2689-2692 - [c14]Hugo Van hamme:
Two correction models for likelihoods in robust speech recognition using missing feature theory. INTERSPEECH 2003: 3073-3076 - [c13]Hugo Van hamme:
Robust speech recognition using missing feature theory in the cepstral or LDA domain. INTERSPEECH 2003: 3089-3092 - [c12]Hugo Van hamme:
Correction of likelihoods for degrees of freedom in robust speech recognition using missing feature theory. ISSPA (1) 2003: 401-404 - 2002
- [c11]Jim Van Sciver, Jeff Z. Ma, Filiep Vanpoucke, Hugo Van hamme:
Investigation of speech recognition over IP channels. ICASSP 2002: 3812-3815 - 2000
- [c10]Christophe Couvreur, Hugo Van hamme:
Model-based feature enhancement for noisy speech recognition. ICASSP 2000: 1719-1722 - [c9]Elena Tsiporkova, Filiep Vanpoucke, Hugo Van hamme:
Evaluation of various confidence-based strategies for isolated word rejection. ICASSP 2000: 1819-1822 - [c8]Frédéric Beaugendre, Tom Claes, Hugo Van hamme:
Dialect adaptation for Mandarin Chinese speech recognition. INTERSPEECH 2000: 803-806
1990 – 1999
- 1999
- [c7]Wei Xu, Jacques Duchateau, Kris Demuynck, Ioannis Dologlou, Patrick Wambacq, Dirk Van Compernolle, Hugo Van hamme:
Accuracy versus complexity in context dependent phone modeling. EUROSPEECH 1999 - 1998
- [c6]Ioannis Dologlou, Tom Claes, Louis ten Bosch, Dirk Van Compernolle, Hugo Van hamme:
Speaker normalization for automatic speech recognition - An on-line approach. EUSIPCO 1998: 1-4 - [c5]Hong Xu, Frédéric Beaugendre, Hugo Van hamme:
Adapting Western Language Recognizer for Chinese Recognition. ISCSLP 1998 - 1996
- [j5]Gerd Vandersteen, Hugo Van hamme, Rik Pintelon:
General framework for asymptotic properties of generalized weighted nonlinear least-squares estimators with deterministic and stochastic weighting. IEEE Trans. Autom. Control. 41(10): 1501-1507 (1996) - [c4]Hugo Van hamme, Filip Van Aelten:
An adaptive-beam pruning technique for continuous speech recognition. ICSLP 1996: 2083-2086 - 1994
- [j4]Johan Schoukens, Rik Pintelon, Hugo Van hamme:
Identification of linear dynamic systems using piecewise constant excitations: Use, misuse and alternatives. Autom. 30(7): 1153-1169 (1994) - [j3]Rik Pintelon, Patrick Guillaume, Yves Rolain, Johan Schoukens, Hugo Van hamme:
Parametric identification of transfer functions in the frequency domain-a survey. IEEE Trans. Autom. Control. 39(11): 2245-2260 (1994) - [c3]Hugo Van hamme:
ARDOSS: autoregressive domain spectral subtraction for robust speech recognition in additive noise. ICSLP 1994: 1019-1022 - [c2]Hugo Van hamme, Guido Gallopyn, Ludwig Weynants, Bart D'hoore, Hervé Bourlard:
Comparison of acoustic features and robustness tests of a real-time recogniser using a hardware telephone line simulator. ICSLP 1994: 1907-1910 - 1991
- [j2]Hugo Van hamme:
Maximum likelihood estimation of superimposed complex sinusoids in white Gaussian noise by reduced effort coarse search (RECS). IEEE Trans. Signal Process. 39(2): 536-538 (1991) - [j1]Hugo Van hamme:
A stochastical limit to the resolution of least squares estimation of the frequencies of a double complex sinusoid. IEEE Trans. Signal Process. 39(12): 2652-2658 (1991)
1980 – 1989
- 1988
- [c1]Michael Unser, Hugo Van hamme, Patrick de Muynck, E. Van Denhaute, Jan Cornelis:
Karhunen-Loeve analysis of dynamic sequences of thermographic images for early breast cancer detection. CVPR 1988: 592-596
Coauthor Index
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