{"title":"基于半监督训练数据的大规模深度神经网络声学建模用于YouTube视频转录","authors":"H. Liao, E. McDermott, A. Senior","doi":"10.1109/ASRU.2013.6707758","DOIUrl":null,"url":null,"abstract":"YouTube is a highly visited video sharing website where over one billion people watch six billion hours of video every month. Improving accessibility to these videos for the hearing impaired and for search and indexing purposes is an excellent application of automatic speech recognition. However, YouTube videos are extremely challenging for automatic speech recognition systems. Standard adapted Gaussian Mixture Model (GMM) based acoustic models can have word error rates above 50%, making this one of the most difficult reported tasks. Since 2009, YouTube has provided automatic generation of closed captions for videos detected to have English speech; the service now supports ten different languages. This paper describes recent improvements to the original system, in particular the use of owner-uploaded video transcripts to generate additional semi-supervised training data and deep neural networks acoustic models with large state inventories. Applying an “island of confidence” filtering heuristic to select useful training segments, and increasing the model size by using 44,526 context dependent states with a low-rank final layer weight matrix approximation, improved performance by about 13% relative compared to previously reported sequence trained DNN results for this task.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"192","resultStr":"{\"title\":\"Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription\",\"authors\":\"H. Liao, E. McDermott, A. Senior\",\"doi\":\"10.1109/ASRU.2013.6707758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"YouTube is a highly visited video sharing website where over one billion people watch six billion hours of video every month. Improving accessibility to these videos for the hearing impaired and for search and indexing purposes is an excellent application of automatic speech recognition. However, YouTube videos are extremely challenging for automatic speech recognition systems. Standard adapted Gaussian Mixture Model (GMM) based acoustic models can have word error rates above 50%, making this one of the most difficult reported tasks. Since 2009, YouTube has provided automatic generation of closed captions for videos detected to have English speech; the service now supports ten different languages. This paper describes recent improvements to the original system, in particular the use of owner-uploaded video transcripts to generate additional semi-supervised training data and deep neural networks acoustic models with large state inventories. Applying an “island of confidence” filtering heuristic to select useful training segments, and increasing the model size by using 44,526 context dependent states with a low-rank final layer weight matrix approximation, improved performance by about 13% relative compared to previously reported sequence trained DNN results for this task.\",\"PeriodicalId\":265258,\"journal\":{\"name\":\"2013 IEEE Workshop on Automatic Speech Recognition and Understanding\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"192\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Workshop on Automatic Speech Recognition and Understanding\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASRU.2013.6707758\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASRU.2013.6707758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription
YouTube is a highly visited video sharing website where over one billion people watch six billion hours of video every month. Improving accessibility to these videos for the hearing impaired and for search and indexing purposes is an excellent application of automatic speech recognition. However, YouTube videos are extremely challenging for automatic speech recognition systems. Standard adapted Gaussian Mixture Model (GMM) based acoustic models can have word error rates above 50%, making this one of the most difficult reported tasks. Since 2009, YouTube has provided automatic generation of closed captions for videos detected to have English speech; the service now supports ten different languages. This paper describes recent improvements to the original system, in particular the use of owner-uploaded video transcripts to generate additional semi-supervised training data and deep neural networks acoustic models with large state inventories. Applying an “island of confidence” filtering heuristic to select useful training segments, and increasing the model size by using 44,526 context dependent states with a low-rank final layer weight matrix approximation, improved performance by about 13% relative compared to previously reported sequence trained DNN results for this task.