activity
20172021
most citedCitrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition

43 citations · 56 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CL20214 cited

SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services

Yang Zhang, Vahid Noroozi, Evelina Bakhturina +1

Dialogue state tracking is an essential part of goal-oriented dialogue systems, while most of these state tracking models often fail to handle unseen services. In this paper, we pr…

cs.CL20219 cited

SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition

Patrick K. O'Neill, Vitaly Lavrukhin, Somshubra Majumdar +10

In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitaliza…

eess.AS202143 cited

Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition

Somshubra Majumdar, Jagadeesh Balam, Oleksii Hrinchuk +3

We propose Citrinet - a new end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model. Citrinet is deep residual neural mo…

eess.IV2021

I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications

Nooshin Mojab, Vahid Noroozi, Abdullah Aleem +8

Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to deve…

cs.CV2020

Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings

Nooshin Mojab, Vahid Noroozi, Darvin Yi +4

With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. However, generalizations to com…

cs.LG2019

Leveraging Semi-Supervised Learning for Fairness using Neural Networks

Vahid Noroozi, Sara Bahaadini, Samira Sheikhi +2

There has been a growing concern about the fairness of decision-making systems based on machine learning. The shortage of labeled data has been always a challenging problem facing…