46 citations · 177 across the 28 of their papers we have counts for
5 papers · 1 filter
On The Inductive Bias of Words in Acoustics-to-Word Models
Hao Tang, James Glass
Acoustics-to-word models are end-to-end speech recognizers that use words as targets without relying on pronunciation dictionaries or graphemes. These models are notoriously diffic…
Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model
Suwon Shon, Hao Tang, James Glass
In this paper, we propose a Convolutional Neural Network (CNN) based speaker recognition model for extracting robust speaker embeddings. The embedding can be extracted efficiently…
On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition
Hao Tang, James Glass
Recurrent neural networks have been the dominant models for many speech and language processing tasks. However, we understand little about the behavior and the class of functions r…
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition
Wei-Ning Hsu, Hao Tang, James Glass
The current trend in automatic speech recognition is to leverage large amounts of labeled data to train supervised neural network models. Unfortunately, obtaining data for a wide r…
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition
Hao Tang, Wei-Ning Hsu, Francois Grondin +1
Speech recognizers trained on close-talking speech do not generalize to distant speech and the word error rate degradation can be as large as 40% absolute. Most studies focus on ta…