Publications of project SHISSM
2022
| Autoencoders Reloaded, and , in: Springer Biological Cybernetics, 2022 |
[DOI] [URL] |
| Efficient Transformer-Based Speech Recognition, , École polytechnique fédérale de Lausanne, 2022 |
[DOI] |
| From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition, and , in: Proceedings of Interspeech Conference, 2022 |
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| SPARSE AUTOENCODERS TO ENHANCE SPEECH RECOGNITION, and , Idiap-RR-10-2022 |
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| SPEECH MODELING USING SPARSE AUTOENCODERS, and , Idiap-RR-11-2022 |
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2021
| Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model, , and , Idiap-RR-04-2021 |
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| Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model, , and , in: Proceedings of Interspeech, 2021 |
[URL] |
| LATTICE-FREE MMI ADAPTATION OF SELF-SUPERVISED PRETRAINED ACOUSTIC MODELS, , and , in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021 |
[URL] |
2020
| Fast Transformers with Clustered Attention, , and , in: Proceedings of the International Conference on Neural Information Processing Systems, 2020 |
| LATTICE-FREE MMI ADAPTATION OF SELF-SUPERVISED PRETRAINED ACOUSTIC MODELS, , and , Idiap-RR-40-2020 |
[URL] |
2019
| ANALYZING UNCERTAINTIES IN SPEECH RECOGNITION USING DROPOUT, , , and , in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019 |
| Unbiased semi-supervised LF-MMI training using dropout, , , and , in: Proceedings of Interspeech 2019, 2019 |
[DOI] |