LOTUS-BI: a Thai-English Code-mixing Speech Corpus

被引：0

作者：

Thatphithakkul, Sumonmas ^{[1
]}

Chunwijitra, Vataya ^{[1
]}

Sertsi, Phuttapong ^{[1
]}

Chootrakool, Patcharika ^{[1
]}

Kasuriya, Sawit ^{[1
]}

机构：

[1] Natl Sci & Technol Dev Agcy NSTDA, Speech & Text Understanding Res Team, Artificial Intelligence Res Unit, NECTEC, 112 Phahonyothin Rd, Khlong Luang 12120, Pathumthani, Thailand

来源：

2019 22ND CONFERENCE OF THE ORIENTAL COCOSDA INTERNATIONAL COMMITTEE FOR THE CO-ORDINATION AND STANDARDISATION OF SPEECH DATABASES AND ASSESSMENT TECHNIQUES (O-COCOSDA) | 2019年

关键词：

Thai-English speech corpus; code-mixing; speech corpus;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays, English words mixed in Thai speech are usually found in a typical speaking style. Consequently, to increase the performance of the speech recognition system, a Thai-English code-mixing speech corpus is required. This paper describes the design and construction of LOTUS-BI corpus: a Thai-English code-mixing speech corpus aimed to be the essential speech database for training acoustic model and language model in order to obtain the better speech recognition accuracy. LOTUS-BI corpus contains 16.5 speech hours from 4 speech tasks: interview, talk, seminar, and meeting. Now, 11.5 speech hours of data from the interview, talk, and seminar acquire from the internet have been transcribed and annotated. Whereas, the rest of 5 speech hours from meeting task has been transcribing. Therefore, only 11.5 speech hours of data were analyzed in this paper. Furthermore, the pronunciation dictionary of vocabularies from LOTUS-BI corpus is created based on Thai phoneme set. The statistical analysis of LOTUS-BI corpus revealed that there are 37.96% of code-mixing utterances, including 34.23% intrasentential and 3.73% inter-sentential utterances. The occurrence of English vocabularies is 29.04% of the total vocabularies in the corpus. Besides, nouns are found in 90% of all English vocabularies in the corpus and 10% in the other grammatical categories.

引用

页码：40 / 44

页数：5