1. Progress Report - V Ravi Chander
ASR for the Elderly
Progress Report
- V Ravi Chander

2. Organisation of Talk Goal and Research Questions Experimental set-up
Results
Next Steps

3. Goal
Project : MATCH (Mobilizing Advanced Technologies for Care at Home)
Role : ASR for spoken dialogue system in home care environment for elderly people.

4. -different demographics
Research Questions (1)
Adaptation
Reduce the amount of annotation required.
Existing Recogniser
-different domain
-different demographics
MATCH

5. Research Questions (2)
Use corpus from a different domain to improve performance in the target domain.

6. Approach Only a small corpus of MATCH exists.
Validate the ideas using AMI corpus.

7. Experimental Set-up AMI 1 AMI 2 AMI 3 AMI 4 CTS-NIST BASELINE
Adapted ASR
Test
AMI 5

8. Sampling Techniques Random Supervised (likelihood based) Speaker level
Utterance level
Unsupervised
GMM based closest speakers
Utterances selected based on potential informativeness

9. GMM based closest Speakers
No of speakers: 136
GMM for each speaker
Feature vectors used : 13 MFCC coefficients
Only speech frames used for modelling
No of Gaussian components : 32

10. Distance measure KL divergence between Gaussians Distance between GMMsf g

11. Clustering (using CluTo)

12. Clustering Results Good pattern seen in age:
If seen at the top two clusters:
Females: 77.3%
Males: 62.5%
If seen at the level of 4 clusters (regrouping according to known information):
Females: 92.45%
Males: 98.79%
No pattern seen with respect to age.

13. Next steps (1)
Create one GMM from half of AMI 5 test speakers data (AMI 5).
Sample the closest speakers from AMI 3 and AMI 4 to the above GMM, to adapt the baseline recognizer.
Test against the other half.
Repeat the experiment interchanging the two halves.

14. Next Steps (2)
Utterance sampling based on the potential informativeness of the sample.
Data Buffer
Baseline
ASR
Adaptation
corpus
ranking
sampling
labelling

15. Thank you!!
Questions / Suggestions