1. Missing feature theory
Statistical estimation of unreliable features for robust speech
recognition
2) Missing feature theory and probabilistic estimation of clean
speech components for robust speech recognition
3) State based imputation of missing data for robust speech
recognition and speech enhancement
4) Missing data theory,spectral subtraction and signal-to-noise
estimation for robust ASR: an integrated study

2. Parameters used in speech recognition can be
Introduction
Parameters used in speech recognition can be
divided in two subsets
1) reliable or present parameters
2) unreliable or missing parameters

3. Introduction There are 2 problems in the application of missing
data in robust ASR
1) identification of the reliable regions
2) recognition techniques that can deal with
incomplete data

4. Detection of unreliable feature
Method:
(1) negative energy criterion
(2) SNR criterion or

5. Detection of unreliable feature
(3) statistical approach :
noise is considered as normally distributed

6. Noise estimation method in [4]
simple estimation
weighted average estimation
C) second order method
D) Histogram method

7. Accuracy for the three detection methods

8. Recognition with incomplete data
Method (1)
Marginalization : unreliable data are ignored
for a single state model ,the probability to emit
vector is

9. Marginalization

10. Marginalization =1
bounded
marginalization

11. Marginalization In Philippe’s another paper [2] ,the clean parameters
are represented as pdfs and missing parameters are
considered as being uniformly
if 0<x<|Y(w)|
otherwise

12. Recognition with incomplete data
Method (2)
GMM based Imputation : unreliable data are estimated
advantages of the approach are that can be followed
by conventional techniques like cepstral,RASTA
In the estimation process,the GMM means are
used to replace the unreliable features
the means and variances of GMM are compensated
with the additive noise,as in PMC

13. Imputation
using inverse log-normal approximation

14. Imputation
transformed into log-spectral domain :

15. Imputation using the noisy GMM,the weighting factor associated
with each distribution is computed as follows:

16. Imputation Finally,the reliable data are enhanced using a
spectral subtraction and the unreliable data are
replaced by a weighted sum of the GMM means

17. features spetra

18. Discussion Why using GMM in this paper?
A HMM based data imputation has been proposed in
[3], when using time-dependent statistical models,if
an error in the decoding sequence occurs,it can
influence the recognition in the second feature domain
therefore , GMM instead of HMM,but sufficient and
computationally efficient for data imputation

19. Experiments results

20. Experiments results

21. Experiments results

22. Experiments results

23. Experiments results

24. Experiments results

25. Experiments results

26. Experiments results