In this paper, we present the systems that the Natural Language Engineering and Pattern Recognition group (ELiRF) has submitted to the MediaEval 2015 Query by Example Search on Speech Task. All of them are based on a Subsequence Dynamic Time Warping algorithm. The systems use information from outside the task (low-resources systems). http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

1. ELiRF at MediaEval 2015: Query by Example
Search on Speech Task (QUESST)
Sergio Laguna, Marcos Calvo, Llu´ıs-F. Hurtado, Emilio Sanchis
Universitat Polit`ecnica de Val`encia
September 14, 2015

2. Overview of the systems
+ variation on SDTW (average distances)

3. Fusion by language

4. Another thing we tried

5. Another thing we tried... but didn’t work :(

6. Our results
Table 1 : Results obtained for the development set.
System Cnxe Cmin
nxe ATWV MTWV
SDTW-avg 1.0651 0.8677 0.1446 0.1543
SDTW 1.0701 0.8702 0.1404 0.1493
Table 2 : Results obtained for the test set.
System Cnxe Cmin
nxe ATWV MTWV
SDTW-avg 1.0731 0.8751 0.1125 0.1181
SDTW 1.1879 0.9338 0.0449 0.0581

7. Comparing to the other teams (bad news)
We are the 9th team by Cnxe . . .
System Cnxe
NNI 0.7610
SPL-IT-UC 0.7866
BUT 0.8452
GTM-UVIGO 0.9185
IIT-B 0.9536
TUKE 0.9714
SPEED 1.0379
CUNY 1.0674
ELiRF 1.0731
NTU 2.0067

8. Comparing to the other teams (good news!)
. . . but the 4th team by Cmin
nxe .
System Cmin
nxe
NNI 0.7472
SPL-IT-UC 0.7809
BUT 0.8263
ELiRF 0.8751
GTM-UVIGO 0.9046
IIT-B 0.9364
TUKE 0.9530
CUNY 0.9853
SPEED 0.9963
NTU 0.9972

9. Conclusions and future work
Conclusions
In this case, the use of average distances led to better results.
(Only in this case?)
We still have to better calibrate our systems.
Future work
Better handle word reorderings and queries not represented as
a consecutive piece of audio.
Keep working on the approach based on image processing.