activity
20192022
most citedImproving on-device speaker verification using federated learning with privacy

2 citations · 5 across the 4 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL20221 cited

End-to-End Speech Translation for Code Switched Speech

Orion Weller, Matthias Sperber, Telmo Pires +4

Code switching (CS) refers to the phenomenon of interchangeably using words and phrases from different languages. CS can pose significant accuracy challenges to NLP, due to the oft…

cs.CL2020

Consistent Transcription and Translation of Speech

Matthias Sperber, Hendra Setiawan, Christian Gollan +2

The conventional paradigm in speech translation starts with a speech recognition step to generate transcripts, followed by a translation step with the automatic transcripts as inpu…

cs.CL2020

Variational Neural Machine Translation with Normalizing Flows

Hendra Setiawan, Matthias Sperber, Udhay Nallasamy +1

Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on…

cs.CL2020

Speech Translation and the End-to-End Promise: Taking Stock of Where We Are

Matthias Sperber, Matthias Paulik

Over its three decade history, speech translation has experienced several shifts in its primary research themes; moving from loosely coupled cascades of speech recognition and mach…

cs.CL2019

Jointly Learning to Align and Translate with Transformer Models

Sarthak Garg, Stephan Peitz, Udhyakumar Nallasamy +1

The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches. However, on th…