用时滞神经网络评价马来语-英语语码转换语音。

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI:10.21437/SLTU.2018-40

Anand Singh, Tien-Ping Tan

{"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}

引用次数: 3

摘要

本文提出了马来语-英语语码转换语料库的新基线;该模型使用因子形式的时滞神经网络(TDNN-F)构建，与高斯混合模型-隐马尔可夫模型(GMM-HMM)相比，单词错误率(WER)显著降低了28.07%。本文的研究结果也证实了延时神经网络(TDNNs)在编码切换语音中的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Workshop on Spoken Language Technologies for Under-resourced Languages

自引率

0.00%

发文量