@techreport{oai:ipsj.ixsq.nii.ac.jp:00200784,
 author = {Yu, Wang and Hiromitsu, Nishizaki and Akio, Kobayashi and Takehito, Utsuro and Yu, Wang and Hiromitsu, Nishizaki and Akio, Kobayashi and Takehito, Utsuro},
 issue = {5},
 month = {Nov},
 note = {We have been developing extension tools of Kaldi, an automatic speech recognition toolkit, with Python language. A part of this toolkit works as wrapper of Kaldi, therefore, some operations, taking feature extraction and decoding with lattice as examples, are easily performed with Python code. In addition, our tools support training an acoustic model by using deep learning framework, such as Chainer. We evaluated our tools on TIMIT corpus and so on, and got a better ASR performance than other ASR systems in some ways., We have been developing extension tools of Kaldi, an automatic speech recognition toolkit, with Python language. A part of this toolkit works as wrapper of Kaldi, therefore, some operations, taking feature extraction and decoding with lattice as examples, are easily performed with Python code. In addition, our tools support training an acoustic model by using deep learning framework, such as Chainer. We evaluated our tools on TIMIT corpus and so on, and got a better ASR performance than other ASR systems in some ways.},
 title = {Development and Evaluation of Kaldi Extension Tools with Python},
 year = {2019}
}