{"title":"欧洲葡萄牙语广播新闻语音识别","authors":"H. Meinedo, N. Souto, J. Neto","doi":"10.1109/ASRU.2001.1034651","DOIUrl":null,"url":null,"abstract":"This paper describes our work on the development of a large vocabulary continuous speech recognition system applied to a broadcast news task for the European Portuguese language in the scope of the ALERT project. We start by presenting the baseline recogniser AUDIMUS, which was originally developed with a corpus of read newspaper text. This is a hybrid system that uses a combination of phone probabilities generated by several MLPs trained on distinct feature sets. The paper details the modifications introduced in this system, namely in the development of a new language model, the vocabulary and pronunciation lexicon and the training on new data from the ALERT BN corpus currently available. The system trained with this BN corpus achieved 18.4% WER when tested with the F0 focus condition (studio, planed, native, clean), and 35.2% when tested in all focus conditions.","PeriodicalId":118671,"journal":{"name":"IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Speech recognition of broadcast news for the European Portuguese language\",\"authors\":\"H. Meinedo, N. Souto, J. Neto\",\"doi\":\"10.1109/ASRU.2001.1034651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes our work on the development of a large vocabulary continuous speech recognition system applied to a broadcast news task for the European Portuguese language in the scope of the ALERT project. We start by presenting the baseline recogniser AUDIMUS, which was originally developed with a corpus of read newspaper text. This is a hybrid system that uses a combination of phone probabilities generated by several MLPs trained on distinct feature sets. The paper details the modifications introduced in this system, namely in the development of a new language model, the vocabulary and pronunciation lexicon and the training on new data from the ALERT BN corpus currently available. The system trained with this BN corpus achieved 18.4% WER when tested with the F0 focus condition (studio, planed, native, clean), and 35.2% when tested in all focus conditions.\",\"PeriodicalId\":118671,\"journal\":{\"name\":\"IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASRU.2001.1034651\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASRU.2001.1034651","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Speech recognition of broadcast news for the European Portuguese language
This paper describes our work on the development of a large vocabulary continuous speech recognition system applied to a broadcast news task for the European Portuguese language in the scope of the ALERT project. We start by presenting the baseline recogniser AUDIMUS, which was originally developed with a corpus of read newspaper text. This is a hybrid system that uses a combination of phone probabilities generated by several MLPs trained on distinct feature sets. The paper details the modifications introduced in this system, namely in the development of a new language model, the vocabulary and pronunciation lexicon and the training on new data from the ALERT BN corpus currently available. The system trained with this BN corpus achieved 18.4% WER when tested with the F0 focus condition (studio, planed, native, clean), and 35.2% when tested in all focus conditions.