{"title":"越南LVCSR声学模拟研究进展","authors":"Tuan-Nam Nguyen, Q. Vu","doi":"10.1109/IALP.2009.66","DOIUrl":null,"url":null,"abstract":"In this paper, we present our experiments on the selection of basic phonetic units for the Vietnamese large vocabulary continuous speech recognition (LVCSR). Two acoustic models were compared. The first model has just used vowels or monophthongs as phonemes [2] while the second one, which was proposed in this paper, has explored the use of diphthongs and triphthongs as phonemes as well. The two models were trained and evaluated on a Broadcast News corpus containing 27 hours of acoustic training data and 1 hour of acoustic testing data. Moreover, an 146M-word corpus collection of newspaper was employed for building the language models. Experimental results indicate significant improvements in both word accuracy rate and time-execution. With the second acoustic model, the word accuracy rates reach 86.06% on the best case and the execution time is faster than the real-time.","PeriodicalId":156840,"journal":{"name":"2009 International Conference on Asian Language Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Advances in Acoustic Modeling for Vietnamese LVCSR\",\"authors\":\"Tuan-Nam Nguyen, Q. Vu\",\"doi\":\"10.1109/IALP.2009.66\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present our experiments on the selection of basic phonetic units for the Vietnamese large vocabulary continuous speech recognition (LVCSR). Two acoustic models were compared. The first model has just used vowels or monophthongs as phonemes [2] while the second one, which was proposed in this paper, has explored the use of diphthongs and triphthongs as phonemes as well. The two models were trained and evaluated on a Broadcast News corpus containing 27 hours of acoustic training data and 1 hour of acoustic testing data. Moreover, an 146M-word corpus collection of newspaper was employed for building the language models. Experimental results indicate significant improvements in both word accuracy rate and time-execution. With the second acoustic model, the word accuracy rates reach 86.06% on the best case and the execution time is faster than the real-time.\",\"PeriodicalId\":156840,\"journal\":{\"name\":\"2009 International Conference on Asian Language Processing\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Asian Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2009.66\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2009.66","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Advances in Acoustic Modeling for Vietnamese LVCSR
In this paper, we present our experiments on the selection of basic phonetic units for the Vietnamese large vocabulary continuous speech recognition (LVCSR). Two acoustic models were compared. The first model has just used vowels or monophthongs as phonemes [2] while the second one, which was proposed in this paper, has explored the use of diphthongs and triphthongs as phonemes as well. The two models were trained and evaluated on a Broadcast News corpus containing 27 hours of acoustic training data and 1 hour of acoustic testing data. Moreover, an 146M-word corpus collection of newspaper was employed for building the language models. Experimental results indicate significant improvements in both word accuracy rate and time-execution. With the second acoustic model, the word accuracy rates reach 86.06% on the best case and the execution time is faster than the real-time.