Name
Abstract | ||
|---|---|---|
Automatic systems for practice and exams are essential to support the growing worldwide demand for learning English as an additional language. Assessment of spontaneous spoken English is, however, currently limited in scope due to the difficulty of achieving sufficient automatic speech recognition (ASR) accuracy, "Off-the-shelf" English ASR systems cannot model the exceptionally wide variety of accents, pronunications and recording conditions found in non-native learner data. Limited training data for different first languages (L1s), across all proficiency levels, often with (at most) crowd-sourced transcriptions. limits the performance of ASR systems trained on non-native English learner speech. This paper investigates whether the effect of one source of error in the system, lexical modelling, can be mitigated by using graphemic lexicons in place of phonetic lexicons based on native speaker pronunications. Graphemic-based English ASR is typically worse than phonetic-based due to the irregularity of English spelling-to-pronunciation but here lower word error rates are consistently observed with the graphemic ASR. The effect of using graphemes on automatic assessment is assessed on different grader feature sets: audio and fluency derived features. including some phonetic level features; and phone/grapheme distance features which capture a measure of pronunciation ability. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.21437/Interspeech.2017-978 | 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION |
Keywords | Field | DocType |
graphemic speech recognition, spoken language assessment, automatic grading, non-native speakers | Pronunciation,Training set,Transcription (linguistics),Computer science,Fluency,Grapheme,Speech recognition,Phone,Spoken language,First language | Conference |
ISSN | Citations | PageRank |
2308-457X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kate Knill | 1 | 249 | 28.02 |
| Mark J. F. Gales | 2 | 3905 | 367.45 |
| Konstantinos G. Kyriakopoulos | 3 | 24 | 11.04 |
| Anton Ragni | 4 | 98 | 9.06 |
| Yu Wang | 5 | 59 | 8.66 |