Name
Abstract | ||
|---|---|---|
It has been demonstrated that system fusion can significantly improve the performance of keyword search. In this paper, we compare the performance of several widely-used arithmetic-based fusion methods using different normalization pipeline and try to find the best pipeline. A novel arithmetic-based fusion method is proposed in this work. The method supplies a more effective way to incorporate the number of systems which have non-zero scores for a detection. When tested on the development test dataset of the OpenKWS15 Evaluation, the proposed method achieves the highest maximum term-weighted value (MTWV) and actual term-weighted value (ATWV) among all other arithmetic-based fusion methods. Usually, discriminative fusion methods employing classifiers can outperform arithmetic-based fusion methods. A DNN-based fusion method is explored in this work. After word-burst information is added, the DNN-based fusion method outperforms all other methods. In addition, it is notable that our arithmetic-based method achieves the same MTWV as the DNN-based method. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ASRU.2015.7404799 | 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) |
Keywords | Field | DocType |
system fusion,keyword search,score normalization,DNN | Normalization (statistics),Pattern recognition,Computer science,Keyword search,Fusion,Speech recognition,Artificial intelligence,Discriminative model | Conference |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Lv | 1 | 26 | 11.28 |
| Meng Cai | 2 | 68 | 8.24 |
| Cheng Lu | 3 | 9 | 1.60 |
| Jian Kang | 4 | 15 | 2.66 |
| Like Hui | 5 | 8 | 2.92 |
| Wei-Qiang Zhang | 6 | 136 | 31.22 |
| Jia Liu | 7 | 277 | 50.34 |