Name
Abstract | ||
|---|---|---|
Short utterances dialect identification is a challenging task because of the substantial similarity between dialects. The previous cross-entropy loss function does not consider the category and probability of prediction error, which result in insensitivity to easily misclassified and unbalanced samples. To solve this problem, we propose to use an improved cross-entropy loss function, namely focal loss, introducing category weights and tunable focusing parameter to improve the classification accuracy. Experiments are carried out on AI Dialect Contest database. The results demonstrate that our proposed end-to-end model trained with focal loss achieves better performance than the model trained with cross-entropy loss function. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/APSIPAASC47483.2019.9023354 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference |
DocType | ISSN | Citations |
Conference | 2309-9402 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiuxian Zhang | 1 | 0 | 0.34 |
| Jiangyan Yi | 2 | 19 | 17.99 |
| Jianhua Tao | 3 | 848 | 138.00 |
| Mingliang Gu | 4 | 0 | 2.03 |
| Yong Ma | 5 | 0 | 1.35 |