{"title":"用时滞神经网络评价马来语-英语语码转换语音。","authors":"Anand Singh, Tien-Ping Tan","doi":"10.21437/SLTU.2018-40","DOIUrl":null,"url":null,"abstract":"This paper presents a new baseline for Malay-English code-switched speech corpus; which is constructed using a factored form of time delay neural networks (TDNN-F), which reflected a significant relative percentage reduction of 28.07% in the word-error rate (WER), as compared to the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The presented results also confirm the effectiveness of time delay neural networks (TDNNs) for code-switched speech.","PeriodicalId":190269,"journal":{"name":"Workshop on Spoken Language Technologies for Under-resourced Languages","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Evaluating Code-Switched Malay-English Speech Using Time Delay Neural Networks.\",\"authors\":\"Anand Singh, Tien-Ping Tan\",\"doi\":\"10.21437/SLTU.2018-40\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new baseline for Malay-English code-switched speech corpus; which is constructed using a factored form of time delay neural networks (TDNN-F), which reflected a significant relative percentage reduction of 28.07% in the word-error rate (WER), as compared to the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The presented results also confirm the effectiveness of time delay neural networks (TDNNs) for code-switched speech.\",\"PeriodicalId\":190269,\"journal\":{\"name\":\"Workshop on Spoken Language Technologies for Under-resourced Languages\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Spoken Language Technologies for Under-resourced Languages\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21437/SLTU.2018-40\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Spoken Language Technologies for Under-resourced Languages","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/SLTU.2018-40","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluating Code-Switched Malay-English Speech Using Time Delay Neural Networks.
This paper presents a new baseline for Malay-English code-switched speech corpus; which is constructed using a factored form of time delay neural networks (TDNN-F), which reflected a significant relative percentage reduction of 28.07% in the word-error rate (WER), as compared to the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The presented results also confirm the effectiveness of time delay neural networks (TDNNs) for code-switched speech.
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