{"title":"Merlin: An Open Source Neural Network Speech Synthesis System","authors":"Zhizheng Wu, O. Watts, Simon King","doi":"10.21437/SSW.2016-33","DOIUrl":null,"url":null,"abstract":"We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis. The system takes linguistic features as input, and employs neural networks to predict acoustic features, which are then passed to a vocoder to produce the speech waveform. Various neural network architectures are implemented, including a standard feedforward neural network, mixture density neural network, recurrent neural network (RNN), long short-term memory (LSTM) recurrent neural network, amongst others. The toolkit is Open Source, written in Python, and is extensible. This paper briefly describes the system, and provides some benchmarking results on a freely-available corpus.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"320","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Speech Synthesis Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/SSW.2016-33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 320
Abstract
We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis. The system takes linguistic features as input, and employs neural networks to predict acoustic features, which are then passed to a vocoder to produce the speech waveform. Various neural network architectures are implemented, including a standard feedforward neural network, mixture density neural network, recurrent neural network (RNN), long short-term memory (LSTM) recurrent neural network, amongst others. The toolkit is Open Source, written in Python, and is extensible. This paper briefly describes the system, and provides some benchmarking results on a freely-available corpus.