Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban
This paper describes our work on automatic speech recognition system (ASR) for an under-resourced language, namely the Iban language, which is spoken in Sarawak, a Malaysian Borneo state. To begin this study, we collected 8 hours of speech data due to no resources yet for ASR concerning this lang...
| Main Authors: | , , , |
|---|---|
| Format: | Proceeding |
| Language: | English |
| Published: |
2014
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/8881/ http://ir.unimas.my/id/eprint/8881/1/COCOSDA-sarahsamsonjuan.pdf |
| _version_ | 1848836463388524544 |
|---|---|
| author | Juan, Sarah Samson Besacier, Laurent Lecouteux, Benjamin Tan, Tien-Ping |
| author_facet | Juan, Sarah Samson Besacier, Laurent Lecouteux, Benjamin Tan, Tien-Ping |
| author_sort | Juan, Sarah Samson |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | This paper describes our work on automatic speech recognition system (ASR) for an under-resourced language, namely the Iban language, which is spoken in Sarawak, a
Malaysian Borneo state. To begin this study, we collected
8 hours of speech data due to no resources yet for ASR
concerning this language. Following the lack of resources, we employed bootstrapping techniques on a closely-related language to build the Iban system. For this case, we utilized Malay data to bootstrap the grapheme-to-phoneme system (G2P) for the target language. We also developed several G2Ps to acquire Iban pronunciation dictionaries, which were later evaluated on the Iban ASR for obtaining the best version. Subsequently, we conducted experiments on cross-lingual
ASR by using subspace Gaussian Mixture Models (SGMM)
where the shared parameters obtained in either monolingual or multilingual fashion. From our observations, using out-of-language data as source language provided lower WER when Iban data is very imited. |
| first_indexed | 2025-11-15T06:24:10Z |
| format | Proceeding |
| id | unimas-8881 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:24:10Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-88812015-10-16T01:22:09Z http://ir.unimas.my/id/eprint/8881/ Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban Juan, Sarah Samson Besacier, Laurent Lecouteux, Benjamin Tan, Tien-Ping Q Science (General) QA75 Electronic computers. Computer science This paper describes our work on automatic speech recognition system (ASR) for an under-resourced language, namely the Iban language, which is spoken in Sarawak, a Malaysian Borneo state. To begin this study, we collected 8 hours of speech data due to no resources yet for ASR concerning this language. Following the lack of resources, we employed bootstrapping techniques on a closely-related language to build the Iban system. For this case, we utilized Malay data to bootstrap the grapheme-to-phoneme system (G2P) for the target language. We also developed several G2Ps to acquire Iban pronunciation dictionaries, which were later evaluated on the Iban ASR for obtaining the best version. Subsequently, we conducted experiments on cross-lingual ASR by using subspace Gaussian Mixture Models (SGMM) where the shared parameters obtained in either monolingual or multilingual fashion. From our observations, using out-of-language data as source language provided lower WER when Iban data is very imited. 2014-09 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/8881/1/COCOSDA-sarahsamsonjuan.pdf Juan, Sarah Samson and Besacier, Laurent and Lecouteux, Benjamin and Tan, Tien-Ping (2014) Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban. In: COCOSDA 2014, Phuket, Thailand. |
| spellingShingle | Q Science (General) QA75 Electronic computers. Computer science Juan, Sarah Samson Besacier, Laurent Lecouteux, Benjamin Tan, Tien-Ping Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban |
| title | Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban |
| title_full | Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban |
| title_fullStr | Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban |
| title_full_unstemmed | Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban |
| title_short | Using Closely-related Language to Build an ASR for a Very Under-resourced Language: Iban |
| title_sort | using closely-related language to build an asr for a very under-resourced language: iban |
| topic | Q Science (General) QA75 Electronic computers. Computer science |
| url | http://ir.unimas.my/id/eprint/8881/ http://ir.unimas.my/id/eprint/8881/1/COCOSDA-sarahsamsonjuan.pdf |