387 citations · 1.3k across the 51 of their papers we have counts for
8 papers · 2 filters
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition
Qiujia Li, Yu Zhang, David Qiu +3
As end-to-end automatic speech recognition (ASR) models reach promising performance, various downstream tasks rely on good confidence estimators for these systems. Recent research…
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, Daniel S. Park, Wei Han +23
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a m…
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
Nanxin Chen, Yu Zhang, Heiga Zen +4
This paper introduces WaveGrad 2, a non-autoregressive generative model for text-to-speech synthesis. WaveGrad 2 is trained to estimate the gradient of the log conditional density…
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction
David Qiu, Yanzhang He, Qiujia Li +3
Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems. Recent works have proposed using neural networks to learn word or utter…
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models
Zhiyun Lu, Wei Han, Yu Zhang +1
Although end-to-end automatic speech recognition (e2e ASR) models are widely deployed in many applications, there have been very few studies to understand models' robustness agains…
Pushing the Limits of Non-Autoregressive Speech Recognition
Edwin G. Ng, Chung-Cheng Chiu, Yu Zhang +1
We combine recent advancements in end-to-end speech recognition to non-autoregressive automatic speech recognition. We push the limits of non-autoregressive state-of-the-art result…