Name
Abstract | ||
|---|---|---|
End-to-end speech recognition has achieved good recognition performance on standard English pronunciation datasets. However, one prominent problem with end-to-end speech recognition systems is that non-native English speakers tend to have complex and varied accents, which reduces the accuracy of English speech recognition in different countries. In order to grapple with such an issue, we first investigate and improve the current mainstream end-to-end multi-accent speech recognition technologies. In addition, we propose two unsupervised accent modelling methods, which convert accent information into a global embedding, and use it to improve the performance of the end-to-end multi-accent speech recognition systems. Experimental results on accented English datasets of eight countries (AESRC2020) show that, compared with the Transformer baseline, our proposed methods achieve relative 14.8% and 15.4% average word error rate (WER) reduction in the development set and evaluation set, respectively. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414833 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
End-to-end, speech recognition, multi-accent, global embedding | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Li | 1 | 0 | 1.69 |
| Beibei Ouyang | 2 | 0 | 0.34 |
| Dexin Liao | 3 | 0 | 1.01 |
| Shipeng Xia | 4 | 0 | 1.01 |
| Lin Li | 5 | 12 | 4.60 |
| Q. Y. Hong | 6 | 50 | 15.79 |