{"title":"巴维埃卡开源语音识别工具包","authors":"Daniel Bolaños","doi":"10.1109/SLT.2012.6424249","DOIUrl":null,"url":null,"abstract":"This article describes the design of Bavieca, an open-source speech recognition toolkit intended for speech research and system development. The toolkit supports lattice-based discriminative training, wide phonetic-context, efficient acoustic scoring, large n-gram language models, and the most common feature and model transformations. Bavieca is written entirely in C++ and presents a simple and modular design with an emphasis on scalability and reusability. Bavieca achieves competitive results in standard benchmarks. The toolkit is distributed under the highly unrestricted Apache 2.0 license, and is freely available on SourceForge.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"The Bavieca open-source speech recognition toolkit\",\"authors\":\"Daniel Bolaños\",\"doi\":\"10.1109/SLT.2012.6424249\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article describes the design of Bavieca, an open-source speech recognition toolkit intended for speech research and system development. The toolkit supports lattice-based discriminative training, wide phonetic-context, efficient acoustic scoring, large n-gram language models, and the most common feature and model transformations. Bavieca is written entirely in C++ and presents a simple and modular design with an emphasis on scalability and reusability. Bavieca achieves competitive results in standard benchmarks. The toolkit is distributed under the highly unrestricted Apache 2.0 license, and is freely available on SourceForge.\",\"PeriodicalId\":375378,\"journal\":{\"name\":\"2012 IEEE Spoken Language Technology Workshop (SLT)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Spoken Language Technology Workshop (SLT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SLT.2012.6424249\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Spoken Language Technology Workshop (SLT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SLT.2012.6424249","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Bavieca open-source speech recognition toolkit
This article describes the design of Bavieca, an open-source speech recognition toolkit intended for speech research and system development. The toolkit supports lattice-based discriminative training, wide phonetic-context, efficient acoustic scoring, large n-gram language models, and the most common feature and model transformations. Bavieca is written entirely in C++ and presents a simple and modular design with an emphasis on scalability and reusability. Bavieca achieves competitive results in standard benchmarks. The toolkit is distributed under the highly unrestricted Apache 2.0 license, and is freely available on SourceForge.