Abstract
Implementation of legal bilingualism in Hong Kong after 1997 has necessitated the production of voluminous and extensive court proceedings and judgments in both Chinese and English. For the former, Cantonese, a dialect of Chinese, is the home language of more than 90% of the population in Hong Kong and so used in the courts. To record speech in Cantonese verbatim, a Chinese Computer-Aided Transcription system has been developed. The transcription system converts stenographic codes into Chinese text, i.e. from phonetic to orthographic representation of the language. The main challenge lies in the resolution of the sever ambiguity resulting from homocode problems in the conversion process. Cantonese Chinese is typified by problematic homonymy, which presents serious challenges. The N-gram statistical model is employed to estimate the most probable character string of the input transcription codes. Domain-specific corpora have been compiled to support the statistical computation. To improve accuracy, scalable techniques such as domain-specific transcription and special encoding are used. Put together, these techniques deliver 96% transcription accuracy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006 |
| Publisher | European Language Resources Association (ELRA) |
| Pages | 2122-2125 |
| DOIs | |
| Publication status | Published - 2006 |
| Event | 5th International Conference on Language Resources and Evaluation, LREC 2006 - Genoa, Italy Duration: 22 May 2006 → 28 May 2006 |
Publication series
| Name | Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006 |
|---|
Conference
| Conference | 5th International Conference on Language Resources and Evaluation, LREC 2006 |
|---|---|
| Place | Italy |
| City | Genoa |
| Period | 22/05/06 → 28/05/06 |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This study was supported by a Competitive Earmarked Research Grant (CERG) from the Research Grant Council of Hong Kong (CityU 1238/00H).
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