M
Michael L. Seltzer
Researcher at Facebook
Publications - 192
Citations - 10401
Michael L. Seltzer is an academic researcher from Facebook. The author has contributed to research in topics: Word error rate & Acoustic model. The author has an hindex of 43, co-authored 184 publications receiving 8829 citations. Previous affiliations of Michael L. Seltzer include Carnegie Mellon University & Microsoft.
Papers
More filters
Proceedings ArticleDOI
Recent advances in deep learning for speech research at Microsoft
Li Deng,Jinyu Li,Jui-Ting Huang,Kaisheng Yao,Dong Yu,Frank Seide,Michael L. Seltzer,Geoff Zweig,Xiaodong He,Jason D. Williams,Yifan Gong,Alex Acero +11 more
TL;DR: An overview of the work by Microsoft speech researchers since 2009 is provided, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology.
Proceedings ArticleDOI
A study on data augmentation of reverberant speech for robust speech recognition
TL;DR: It is found that the performance gap between using simulated and real RIRs can be eliminated when point-source noises are added, and the trained acoustic models not only perform well in the distant- talking scenario but also provide better results in the close-talking scenario.
Proceedings ArticleDOI
An investigation of deep neural networks for noise robust speech recognition
TL;DR: The noise robustness of DNN-based acoustic models can match state-of-the-art performance on the Aurora 4 task without any explicit noise compensation and can be further improved by incorporating information about the environment into DNN training using a new method called noise-aware training.
Posted Content
Achieving Human Parity in Conversational Speech Recognition
Wayne Xiong,Jasha Droppo,Xuedong Huang,Frank Seide,Michael L. Seltzer,Andreas Stolcke,Dong Yu,Geoffrey Zweig +7 more
TL;DR: The human error rate on the widely used NIST 2000 test set is measured, and the latest automated speech recognition system has reached human parity, establishing a new state of the art, and edges past the human benchmark.
An Introduction to Computational Networks and the Computational Network Toolkit
Dong Yu,Adam Eversole,Michael L. Seltzer,Kaisheng Yao,Oleksii Kuchaiev,Yu Zhang,Frank Seide,Zhiheng Huang,Brian Guenter,Huaming Wang,Jasha Droppo,Geoffrey Zweig,Christopher J. Rossbach,Jie Gao,Andreas Stolcke,Jon Currey,Malcolm Slaney,Guoguo Chen,Amit Kumar Agarwal,Christopher H. Basoglu,Marko Padmilac,Alexey Kamenev,Vladimir Ivanov,Scott Cypher,Hari Parthasarathi,Bhaskar Mitra,Baolin Peng,Xuedong Huang +27 more
TL;DR: The computational network toolkit (CNTK), an implementation of CN that supports both GPU and CPU, is introduced and the architecture and the key components of the CNTK are described, the command line options to use C NTK, and the network definition and model editing language are described.