This document discusses using compressed sensing for noise robust speech recognition. It summarizes that speech can be represented sparsely using a basis of examples. When there is missing data due to noise, compressed sensing allows reconstructing the speech signal from a sparse representation even if much of the data is missing. It also notes that for this approach to work well in speech recognition, accurately estimating the mask of missing data is important.

1. Noise robust speech recognition through Compressed Sensing Jort Gemmeke Bert Cranen Centre for Language and Speech Technology Radboud University Nijmegen – The Netherlands

2. Speech recognition “ one” Speech recognition Feature extraction

3. Speech recognition “ one” Speech recognition Feature extraction ?? Speech recognition Feature extraction

4. Noise robust speech recognition “ one” Speech recognition Speech restoration Feature extraction pre- processing

5. Missing Data Imputation Clean speech Noisy speech Mask Frequency  time  “ one” “ one” time  time 

6. Missing Data Imputation Masked speech Restored Speech Frequency  time  time  Imputation

7. Sparse representation one

8. Sparse representation … one eight four six one zero two seven one four one nine

9. Sparse representation … one eight four six one zero two seven one four one nine

10. Sparse representation … one eight four six one zero two seven one four one nine

13. Compressed Sensing

14. Compressed Sensing … eight four six one zero two one seven four one nine

15. Compressed Sensing … eight four six one zero two one seven four one nine

16. Imputation one one four one

17. Imputation one one four one one one four one

18. Imputation one one four one one one four one

20. Results Consonant Challenge Method Test set 1 Clean 2 comp 3 8 spk 4 SSN 5 fact 6 MSSN 7 3 spk baseline 86.7 7.6 5.0 5.5 3.9 8.9 5.5 oracle mask - 44.8 43.0 36.0 39.6 41.7 40.9 estimated mask - 7.3 9.9 7.8 9.6 7.0 6.0

22. Questions… 8

23. Vector representation

24. linear representation

25. Basis of examples

26. Reliable and unreliable part

27. sparse representation

28. sparse representation

29. Sparse Imputation