Name
Title | ||
|---|---|---|
Improve low-resource non-native mispronunciation detection with native speech by articulatory-based tandem feature |
Abstract | ||
|---|---|---|
In this paper, we propose a method to improve detecting the mispronunciation type of the non-native learners. In order to cope with the low-resource condition of non-native speech and the difference of native and non-native speech, the following efforts are made: 1) train acoustic model with the low-resource non-native data; 2) introduce the articulatory-based tandem feature; 3) pool auxiliary native data and non-native data together to train the articulatory-based MLP system. We take Chinese learning English for example, and select 1h speech to imitate the low-resource non-native speech situation. In addition, it's studied the combination of pitch and different articulatory-based tandem feature with different input feature (PLP, MFCC, Fliterbank). The experiments show that the proposed method improves the performance obviously. The phone recognition accuracy is improved by 2.99% and the mispronunciation type accuracy is improved by 2.27%. © 2013 IEEE. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ChinaSIP.2013.6625312 | ChinaSIP |
Field | DocType | Volume |
Speech processing,Mel-frequency cepstrum,Tandem,Pattern recognition,Computer science,Voice activity detection,Speech recognition,Phone,Artificial intelligence,Acoustic model | Conference | null |
Issue | ISSN | Citations |
null | null | 2 |
PageRank | References | Authors |
0.38 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Yuan | 1 | 18 | 2.66 |
| ji | 2 | 8 | 1.14 |
| Junhong Zhao | 3 | 27 | 7.02 |
| Jia Liu | 4 | 277 | 50.34 |