Low-Resource Speech Recognition and Keyword-Spotting
By Mark John Francis Gales, K. Knill, Anton Ragni. 2017
16 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Speech & Audio, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Michael Auli
- Alexei Baevski
- Yu Zhang
- Alexis Conneau
- Graham Neubig
- Luke S. Zettlemoyer
- Mohit Bansal
- Abdel-rahman Mohamed
In Speech & Audio
- Alexei Baevski
- Michael Auli
- Abdel-rahman Mohamed
- Alexis Conneau
- Yu Zhang
- Wei-Ning Hsu
- Ronan Collobert
- Jinyu Li
In Signal Processing
- Alexei Baevski
- Abdel-rahman Mohamed
- Michael Auli
- Yu Zhang
- Alexis Conneau
- Ronan Collobert
- Wei-Ning Hsu
- Jinyu Li
Citations in the corpus
- FLEURS: FEW-Shot Learning Evaluation of Universal Representations of Speech (2022, methodology): “Such recent progress has been made possible with the release of both large-scale pre-training and evaluation datasets like Multilingual LibriSpeech [13], VoxPopuli [14], CoVoST2 [15], CommonVoice [16], and the re-use of existing datasets like BABEL [17].”
- Unsupervised Cross-lingual Representation Learning for Speech Recognition (2020, background): “This has been a long standing interest in the speech community (Byrne et al., 2000; Le & Besacier, 2009; Ghoshal et al., 2013; Huang et al., 2013; Gales et al., 2017; Cho et al., 2018; Seki et al., 2018) which includes systems able to transcribe multiple languages (Burget et al.”