Name
Abstract | ||
|---|---|---|
Existing acoustic models can be transferred to any language with a pronunciation lexicon (lexicon) that uses the same set of sub-word units as in training. Unfortunately such lexicons are not readily available in many low-resource languages. We bypass this requirement and create lexicons by training a grapheme-to-phoneme (G2P) transducer on a subset of words from other languages for which pronunciations are available. The subset of words is selected based on how representative it is of target language text. We find that cross-language acoustic model transfer using our selection strategy outperforms selection based on language similarity, and results in ASR performance approaching that of hand-crafted rule based lexicons in the majority of cases. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ASRU46091.2019.9004019 | 2019 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU 2019) |
Keywords | DocType | Citations |
Pronunciation Lexicon, Cross-language transfer, Submodularity | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Wiesner | 1 | 0 | 0.34 |
| Oliver Adams | 2 | 0 | 0.68 |
| David Yarowsky | 3 | 3986 | 618.81 |
| Jan Trmal | 4 | 235 | 20.91 |
| Sanjeev Khudanpur | 5 | 2155 | 202.00 |