Continuous digit recognition in noise: reservoirs can do an excellent job!
- Author
- Azarakhsh Jalalvand (UGent) , Fabian Triefenbach (UGent) and Jean-Pierre Martens (UGent)
- Organization
- Project
-
- ORGANIC (ORGANIC: Self-organized recurrent neural learing for language processing)
- Abstract
- In this paper a formerly proposed continuous digit recognition system based on Reservoir Computing (RC) is improved in two respects: (1)the single reservoir is substituted by a stack of reservoirs, and (2)the straightforward mapping of reservoir outputs to state likelihoods is replaced by a trained non-parametric mapping. Furthermore, it is shown that a reservoir-based method can improve a model trained on clean speech to work better in a noisy condition from which it has a number of unknown digit string recordings available. The first two improvements have lead to a system that outperforms a HMM-based system with the same noise robust features as input. The model adaptation offers a promising supplementary gain at modest noise levels.
- Keywords
- Noise Robustness, Model Adaptation, Reservoir Computing, Acoustic Modeling
Downloads
-
(...).pdf
- full text
- |
- UGent only
- |
- |
- 410.69 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-3054587
- MLA
- Jalalvand, Azarakhsh, et al. “Continuous Digit Recognition in Noise: Reservoirs Can Do an Excellent Job!” 13th Annual Conference of the International Speech Communication Association, Proceedings, vol. 1–3, International Speech Communication Association (ISCA), 2012, pp. 1802–05.
- APA
- Jalalvand, A., Triefenbach, F., & Martens, J.-P. (2012). Continuous digit recognition in noise: reservoirs can do an excellent job! 13th Annual Conference of the International Speech Communication Association, Proceedings, 1–3, 1802–1805. International Speech Communication Association (ISCA).
- Chicago author-date
- Jalalvand, Azarakhsh, Fabian Triefenbach, and Jean-Pierre Martens. 2012. “Continuous Digit Recognition in Noise: Reservoirs Can Do an Excellent Job!” In 13th Annual Conference of the International Speech Communication Association, Proceedings, 1–3:1802–5. International Speech Communication Association (ISCA).
- Chicago author-date (all authors)
- Jalalvand, Azarakhsh, Fabian Triefenbach, and Jean-Pierre Martens. 2012. “Continuous Digit Recognition in Noise: Reservoirs Can Do an Excellent Job!” In 13th Annual Conference of the International Speech Communication Association, Proceedings, 1–3:1802–1805. International Speech Communication Association (ISCA).
- Vancouver
- 1.Jalalvand A, Triefenbach F, Martens J-P. Continuous digit recognition in noise: reservoirs can do an excellent job! In: 13th Annual conference of the International Speech Communication Association, Proceedings. International Speech Communication Association (ISCA); 2012. p. 1802–5.
- IEEE
- [1]A. Jalalvand, F. Triefenbach, and J.-P. Martens, “Continuous digit recognition in noise: reservoirs can do an excellent job!,” in 13th Annual conference of the International Speech Communication Association, Proceedings, Portland, OR, USA, 2012, vol. 1–3, pp. 1802–1805.
@inproceedings{3054587, abstract = {{In this paper a formerly proposed continuous digit recognition system based on Reservoir Computing (RC) is improved in two respects: (1)the single reservoir is substituted by a stack of reservoirs, and (2)the straightforward mapping of reservoir outputs to state likelihoods is replaced by a trained non-parametric mapping. Furthermore, it is shown that a reservoir-based method can improve a model trained on clean speech to work better in a noisy condition from which it has a number of unknown digit string recordings available. The first two improvements have lead to a system that outperforms a HMM-based system with the same noise robust features as input. The model adaptation offers a promising supplementary gain at modest noise levels.}}, articleno = {{644}}, author = {{Jalalvand, Azarakhsh and Triefenbach, Fabian and Martens, Jean-Pierre}}, booktitle = {{13th Annual conference of the International Speech Communication Association, Proceedings}}, isbn = {{9781622767595}}, keywords = {{Noise Robustness,Model Adaptation,Reservoir Computing,Acoustic Modeling}}, language = {{eng}}, location = {{Portland, OR, USA}}, pages = {{644:1802--644:1805}}, publisher = {{International Speech Communication Association (ISCA)}}, title = {{Continuous digit recognition in noise: reservoirs can do an excellent job!}}, volume = {{1-3}}, year = {{2012}}, }