Quantifying and Maximizing the Benefits of Back-End Noise Adaption on
Attention-Based Speech Recognition Models

Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models

3 May 2021

Coleman Hooper

Papers citing "Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models"

8 / 8 papers shown

Title
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation T. Nguyen S. Stueker Jan Niehues A. Waibel 46 98 0 29 Oct 2019
Clotho: An Audio Captioning Dataset Konstantinos Drossos Samuel Lipping Tuomas Virtanen 72 381 0 21 Oct 2019
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation Brian Thompson Huda Khayrallah Antonios Anastasopoulos Arya D. McCarthy Kevin Duh Rebecca Marvin Paul McNamee Jeremy Gwinnup Tim Anderson Philipp Koehn 47 54 0 14 Sep 2018
Extreme Adaptation for Personalized Neural Machine Translation Paul Michel Graham Neubig 55 104 0 04 May 2018
State-of-the-art Speech Recognition With Sequence-to-Sequence Models Chung-Cheng Chiu Tara N. Sainath Yonghui Wu Rohit Prabhavalkar Patrick Nguyen ... Katya Gonina Navdeep Jaitly Yue Liu J. Chorowski M. Bacchiani AI4TS 84 1,150 0 05 Dec 2017
OpenNMT: Open-Source Toolkit for Neural Machine Translation Guillaume Klein Yoon Kim Yuntian Deng Jean Senellart Alexander M. Rush 316 1,897 0 10 Jan 2017
Listen, Attend and Spell William Chan Navdeep Jaitly Quoc V. Le Oriol Vinyals RALM 136 2,261 0 05 Aug 2015
How transferable are features in deep neural networks? J. Yosinski Jeff Clune Yoshua Bengio Hod Lipson OOD 162 8,309 0 06 Nov 2014