no code implementations • 28 Dec 2019 • Abhinav Garg, Dhananjaya Gowda, Ankur Kumar, Kwangyoun Kim, Mehul Kumar, Chanwoo Kim
In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models.
no code implementations • 14 Dec 2020 • Chanwoo Kim, Dhananjaya Gowda, Dongsoo Lee, Jiyeon Kim, Ankur Kumar, Sungsoo Kim, Abhinav Garg, Changwoo Han
Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder based on a Weighted Finite State Transducer (WFST), and so on.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 17 Aug 2021 • Ankur Kumar, Prasunika Khare, Mayank Goswami
Other performance indices show that FFT method is processing the UCT signal with best recovery.
no code implementations • 25 Mar 2022 • Ankur Kumar
We propose a hybrid compression approach to mitigate this where we compress the attention blocks using low rank approximation and use the previously mentioned pruning with a lower rate for feedforward blocks in each transformer layer.
no code implementations • 8 May 2023 • Dima Rekesh, Nithin Rao Koluguri, Samuel Kriman, Somshubra Majumdar, Vahid Noroozi, He Huang, Oleksii Hrinchuk, Krishna Puvvada, Ankur Kumar, Jagadeesh Balam, Boris Ginsburg
Conformer-based models have become the dominant end-to-end architecture for speech processing tasks.
Ranked #1 on Speech Recognition on Tedlium (using extra training data)
1 code implementation • 27 Dec 2023 • Vahid Noroozi, Somshubra Majumdar, Ankur Kumar, Jagadeesh Balam, Boris Ginsburg
We also showed that training a model with multiple latencies can achieve better accuracy than single latency models while it enables us to support multiple latencies with a single model.
no code implementations • 3 Feb 2024 • Erin Weisbart, Ankur Kumar, John Arevalo, Anne E. Carpenter, Beth A. Cimini, Shantanu Singh
Image-based or morphological profiling is a rapidly expanding field wherein cells are "profiled" by extracting hundreds to thousands of unbiased, quantitative features from images of cells that have been perturbed by genetic or chemical perturbations.