activity
20172020
most citedAcoustic data-driven lexicon learning based on a greedy pronunciation selection framework

5 citations · 14 across the 7 of their papers we have counts for

collaborators

8 papers

eess.AS2020

Neural Language Modeling With Implicit Cache Pointers

Ke Li, Daniel Povey, Sanjeev Khudanpur

A cache-inspired approach is proposed for neural language models (LMs) to improve long-range dependency and better predict rare words from long contexts. This approach is a simpler…

eess.AS20201 cited

Mixture of Speaker-type PLDAs for Children's Speech Diarization

Jiamin Xie, Suzanna Sia, Paola Garcia +2

In diarization, the PLDA is typically used to model an inference structure which assumes the variation in speech segments be induced by various speakers. The speaker variation is t…

cs.CL2020

Efficient MDI Adaptation for n-gram Language Models

Ruizhe Huang, Ke Li, Ashish Arora +2

This paper presents an efficient algorithm for n-gram language model adaptation under the minimum discrimination information (MDI) principle, where an out-of-domain language model…

eess.AS20201 cited

PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR

Yiwen Shao, Yiming Wang, Daniel Povey +1

We present PyChain, a fully parallelized PyTorch implementation of end-to-end lattice-free maximum mutual information (LF-MMI) training for the so-called \emph{chain models} in the…

eess.AS20205 cited

Wake Word Detection with Alignment-Free Lattice-Free MMI

Yiming Wang, Hang Lv, Daniel Povey +2

Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. We present novel methods to train a hybrid DNN/HMM wak…

eess.AS20202 cited

Speaker Diarization with Region Proposal Network

Zili Huang, Shinji Watanabe, Yusuke Fujita +4

Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem. Although the standard diarization systems c…