461 citations · 1.1k across the 22 of their papers we have counts for
5 papers · 1 filter
Differentiable Weighted Finite-State Transducers
Awni Hannun, Vineel Pratap, Jacob Kahn +1
We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time. Through the separation…
Rethinking Evaluation in ASR: Are Our Models Robust Enough?
Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap +5
Is pushing numbers on a single benchmark valuable in automatic speech recognition? Research results in acoustic modeling are typically evaluated based on performance on a single da…
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling
Tatiana Likhomanenko, Qiantong Xu, Jacob Kahn +2
Recent results in end-to-end automatic speech recognition have demonstrated the efficacy of pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Clas…
Iterative Pseudo-Labeling for Speech Recognition
Qiantong Xu, Tatiana Likhomanenko, Jacob Kahn +3
Pseudo-labeling has recently shown promise in end-to-end automatic speech recognition (ASR). We study Iterative Pseudo-Labeling (IPL), a semi-supervised algorithm which efficiently…
Scaling Up Online Speech Recognition Using ConvNets
Vineel Pratap, Qiantong Xu, Jacob Kahn +6
We design an online end-to-end speech recognition system based on Time-Depth Separable (TDS) convolutions and Connectionist Temporal Classification (CTC). We improve the core TDS a…