98 citations · 185 across the 17 of their papers we have counts for
7 papers
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
Shane Settle, Karen Livescu
Acoustic word embeddings --- fixed-dimensional vector representations of variable-length spoken word segments --- have begun to be considered for tasks such as speech recognition a…
Multi-view Recurrent Neural Acoustic Word Embeddings
Wanjia He, Weiran Wang, Karen Livescu
Recent work has begun exploring neural acoustic word embeddings---fixed-dimensional vector representations of arbitrary-length speech segments corresponding to words. Such embeddin…
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…
Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models
Shubham Toshniwal, Karen Livescu
We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that r…
Deep Variational Canonical Correlation Analysis
Weiran Wang, Xinchen Yan, Honglak Lee +1
We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear…
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…