Assistant Research Professor
Primary Appointment: Center for Language and Speech Processing
- Acoustic Modeling
- Pronunciation and stress modeling
- Deep neural networks
- Language modeling
- Weighted Finite State Transducers
Daniel Povey completed his PhD at Cambridge University in 2003, and after spending just under ten years working for industry research labs (IBM Research and then Microsoft Research), joined Johns Hopkins University in 2012. His thesis work introduced several practical innovations for discriminative training of models for speech recognition, and made those techniques widely popular. At IBM Research he introduced feature-space discriminative training, which has become a common feature of state-of-the art systems. He also devised the Subspace Gaussian Mixture Model– a modeling technique which enhances the Gaussian Mixture Model framework by using subspace ideas similar to those used in speaker identification. At Microsoft Research and then at Johns Hopkins University, he has been creating a speech recognition toolkit “Kaldi”, which aims to make state-of-the-art speech recognition techniques widely accessible.