11 citations · 24 across the 6 of their papers we have counts for
5 papers · 1 filter
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
Data augmentation is a technique to generate new training data based on existing data. We evaluate the simple and cost-effective method of concatenating the original data examples…
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language. Such data are notoriously scarce, makin…
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR
Tsz Kin Lam, Mayumi Ohta, Shigehiko Schamoni +1
We propose an on-the-fly data augmentation method for automatic speech recognition (ASR) that uses alignment information to generate effective training samples. Our method, called…
Cascaded Models With Cyclic Feedback For Direct Speech Translation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
Direct speech translation describes a scenario where only speech inputs and corresponding translations are available. Such data are notoriously limited. We present a technique that…
Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
We propose an interactive-predictive neural machine translation framework for easier model personalization using reinforcement and imitation learning. During the interactive transl…