241 citations · 1.2k across the 60 of their papers we have counts for
8 papers · 1 filter
Hierarchical Generative Modeling for Controllable Speech Synthesis
Wei-Ning Hsu, Yu Zhang, Ron J. Weiss +9
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training d…
Unsupervised Representation Learning of Speech for Dialect Identification
Suwon Shon, Wei-Ning Hsu, James Glass
In this paper, we explore the use of a factorized hierarchical variational autoencoder (FHVAE) model to learn an unsupervised latent representation for dialect identification (DID)…
Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis
Yu-An Chung, Yuxuan Wang, Wei-Ning Hsu +2
Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent results, they typically require a sizable set of high-quality <text, audio> pairs for training…
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…
Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data
Wei-Ning Hsu, James Glass
Multimodal sensory data resembles the form of information perceived by humans for learning, and are easy to obtain in large quantities. Compared to unimodal data, synchronization o…
Scalable Factorized Hierarchical Variational Autoencoder Training
Wei-Ning Hsu, James Glass
Deep generative models have achieved great success in unsupervised learning with the ability to capture complex nonlinear relationships between latent generating factors and observ…
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition
Wei-Ning Hsu, James Glass
The performance of automatic speech recognition (ASR) systems can be significantly compromised by previously unseen conditions, which is typically due to a mismatch between trainin…