Fetching the paper…
Reading the bibliography…
Hallucinations are a type of output error produced by deep neural networks.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Detection of confusable words in automatic speech recognition
J. Anguita, J. Hernando, S. Peillon, and A. Bramoulle. 2005 · 2005
Earlier work this paper cites.
Adding noise to improve noise robustness in speech recognition
Nicolás Morales, Liang Gu, and Yuqing Gao. 2007 · 2007
Earlier work this paper cites.
GLEU: Automatic evaluation of sentence-level fluency
Andrew Mutton, Mark Dras, Stephen Wan, and Robert Dale. 2007 · 2007
Earlier work this paper cites.
Discriminative language modeling using simulated asr errors
Preethi Jyothi and Eric Fosler-Lussier. 2010 · 2010
Earlier work this paper cites.
Hallucinated n-best lists for discriminative language modeling
K. Sagae, M. Lehr, E. Prud’hommeaux, P. Xu, N. Glenn, D. Karakos, S. Khudanpur, B. Roark, M. Saraçlar, I. Shafran, D. Bikel, C. Callison-Burch, Y. Cao, K. Hall, E. Hasler, P. Koehn, A. Lopez, M. Post, and D. Riley. 2012 · 2012
Earlier work this paper cites.
Librispeech: An asr corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. 2015 · 2015
Earlier work this paper cites.
Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
Earlier work this paper cites.
Automatic speech recognition errors detection and correction: A review
Rahhal Errattahi, Asmaa El Hannani, and Hassan Ouahmane. 2018 · 2018
Earlier work this paper cites.
Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018 · 2018
Earlier work this paper cites.
Identifying fluently inadequate output in neural and statistical machine translation
Marianna Martindale, Marine Carpuat, Kevin Duh, and Paul McNamee. 2019 · 2019
Cited alongside, same era.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Cited alongside, same era.
Specaugment: A simple data augmentation method for automatic speech recognition
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le. 2019 · 2019
Cited alongside, same era.
Learning from past mistakes: improving automatic speech recognition output via noisy-clean phrase context modeling
Prashanth Gurunath Shivakumar, Haoqi Li, Kevin Knight, and Panayiotis Georgiou. 2019 · 2019
Cited alongside, same era.
Synthetic data augmentation for improving low-resource asr
Bao Thai, Robert Jimerson, Dominic Arcoraci, Emily Prud’hommeaux, and Raymond Ptucha. 2019 · 2019
Cited alongside, same era.
The curious case of hallucinations in neural machine translation
Vikas Raunak, Arul Menezes, and Marcin Junczys-Dowmunt. 2021 · 2021
Later among the works it cites.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Later among the works it cites.
Non-autoregressive Chinese ASR error correction with phonological training
Zheng Fang, Ruiqing Zhang, Zhongjun He, Hua Wu, and Yanan Cao. 2022 · 2022
Later among the works it cites.
How bad are artifacts?: Analyzing the impact of speech enhancement errors on asr
Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki, and Shigeru Katagiri. 2022 · 2022
Later among the works it cites.
Hallucination of speech recognition errors with sequence to sequence learning
Prashant Serai, Vishal Sunder, and Eric Fosler-Lussier. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Common voice: A massively-multilingual speech corpus
Rosana Ardila, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer, Michael Henretty, Reuben Morais, Lindsay Saunders, Francis Tyers, and Gregor Weber. 2020 · 2020
Cited alongside, same era.
wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020 · 2020
Cited alongside, same era.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman. 2020 · 2020
Cited alongside, same era.
Topic model robustness to automatic speech recognition errors in podcast transcripts
Raluca Alexandra Fetic, Mikkel Jordahn, Lucas Chaves Lima, Rasmus Arpe Fogh Egebæk, Martin Carsten Nielsen, Benjamin Biering, and Lars Kai Hansen. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Artificial hallucinations in chatgpt: Implications in scientific writing
Hussam Alkaissi and Samy I McFarlane. 2023 · 2023
Later among the works it cites.
State-of-the-art generalisation research in nlp: A taxonomy and review
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella Sinclair, Dennis Ulmer, Florian Schottmann, Khuyagbaatar Batsuren, Kaiser Sun, Koustuv Sinha, Leila Khalatbari, Maria Ryskina, Rita Frieske, Ryan Cotterell, and Zhijing Jin. 2023 · 2023
Later among the works it cites.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
Later among the works it cites.
Auto-avsr: Audio-visual speech recognition with automatic labels
Pingchuan Ma, Alexandros Haliassos, Adriana Fernandez-Lopez, Honglie Chen, Stavros Petridis, and Maja Pantic. 2023 · 2023
Later among the works it cites.