46 citations · 84 across the 5 of their papers we have counts for
6 papers
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
Achintya kr. Sarkar, Zheng-Hua Tan, Hao Tang +2
There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for im…
An Unsupervised Autoregressive Model for Speech Representation Learning
Yu-An Chung, Wei-Ning Hsu, Hao Tang +1
This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that a…
End-to-End Training Approaches for Discriminative Segmental Models
Hao Tang, Weiran Wang, Kevin Gimpel +1
Recent work on discriminative segmental models has shown that they can achieve competitive speech recognition performance, using features based on deep neural frame classifiers. Ho…
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Taehwan Kim, Jonathan Keane, Weiran Wang +5
We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied par…
Efficient Segmental Cascades for Speech Recognition
Hao Tang, Weiran Wang, Kevin Gimpel +1
Discriminative segmental models offer a way to incorporate flexible feature functions into speech recognition. However, their appeal has been limited by their computational require…
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation
Taehwan Kim, Weiran Wang, Hao Tang +1
We study the problem of recognition of fingerspelled letter sequences in American Sign Language in a signer-independent setting. Fingerspelled sequences are both challenging and im…