83 citations · 132 across the 5 of their papers we have counts for
5 papers
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification
Yuanyuan Wang, Yang Zhang, Zhiyong Wu +4
Data augmentation is vital to the generalization ability and robustness of deep neural networks (DNNs) models. Existing augmentation methods for speaker verification manipulate the…
A Chat About Boring Problems: Studying GPT-based text normalization
Yang Zhang, Travis M. Bartley, Mariana Graterol-Fuenmayor +3
Text normalization - the conversion of text from written to spoken form - is traditionally assumed to be an ill-formed task for language models. In this work, we argue otherwise. W…
Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio
Yang Zhang, Krishna C. Puvvada, Vitaly Lavrukhin +1
We propose CONF-TSASR, a non-autoregressive end-to-end time-frequency domain architecture for single-channel target-speaker automatic speech recognition (TS-ASR). The model consist…
QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions
Samuel Kriman, Stanislav Beliaev, Boris Ginsburg +6
We propose a new end-to-end neural acoustic model for automatic speech recognition. The model is composed of multiple blocks with residual connections between them. Each block cons…
Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks
Boris Ginsburg, Patrice Castonguay, Oleksii Hrinchuk +7
We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay. In our experiments on neural networks for ima…