Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

8 August 2019

Papers citing "Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition"

7 / 7 papers shown

Title
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer Cong-Thanh Do Mohan Li R. Doddipatla 55 3 0 29 Jul 2022
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale Gopinath Chennupati Milind Rao Gurpreet Chadha Aaron Eakin A. Raju ... Andrew Oberlin Buddha Nandanoor Prahalad Venkataramanan Zheng Wu Pankaj Sitpure CLL 97 8 0 19 Jul 2022
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0 Sameer Khurana Antoine Laurent James R. Glass VLM 119 18 0 07 Oct 2021
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System Jinhan Wang Yunzheng Zhu Ruchao Fan Wei Chu Abeer Alwan 69 8 0 18 Jun 2021
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems Juan Pablo Zuluaga Iuliia Nigmatulina Amrutha Prasad P. Motlícek Karel Veselý M. Kocour Igor Szöke 130 27 0 08 Apr 2021
Semi-Supervised Learning with Data Augmentation for End-to-End ASR F. Weninger F. Mana R. Gemello Jesús Andrés-Ferrer P. Zhan 91 30 0 27 Jul 2020
Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition Shaoshi Ling Yuzong Liu Julian Salazar Katrin Kirchhoff SSL 86 139 0 03 Dec 2019