Name
Title | ||
|---|---|---|
Automatic assessment of English proficiency for Japanese learners without reference sentences based on deep neural network acoustic models. |
Abstract | ||
|---|---|---|
•A novel machine score for automatic pronunciation evaluation is proposed: Reference-free Error Rate (RER).•The non-native acoustic models and native ones are combined together as an ASR-based automatic English proficiency evaluation system.•The DNN-based acoustic models significantly improved the accuracy of recognition.•The established evaluation system has the ability to evaluate the utterance from the speaker without knowing the transcription in advance.•The performance of the proposed RER score has a high correlation with human proficiency score. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.specom.2019.12.002 | Speech Communication |
Keywords | Field | DocType |
Automatic proficiency assessment,Non-native speech,Speech recognition,Acoustic models,Deep neural network (DNN),Japanese learners,Computer-assisted language learning (CALL) | Pronunciation,Language proficiency,Computer science,Word error rate,Utterance,Speech recognition,Artificial neural network,Hidden Markov model,Sentence,Acoustic model | Journal |
Volume | ISSN | Citations |
116 | 0167-6393 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiang Fu | 1 | 0 | 0.34 |
| Yuya Chiba | 2 | 8 | 6.96 |
| Takashi Nose | 3 | 399 | 39.82 |
| Akinori Ito | 4 | 272 | 62.32 |