activity
20182022
most citedLattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models

6 citations · 6 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CL2022

Towards Zero-Shot Code-Switched Speech Recognition

Brian Yan, Matthew Wiesner, Ondrej Klejch +2

In this work, we seek to build effective code-switched (CS) automatic speech recognition systems (ASR) under the zero-shot setting where no transcribed CS speech data is available…

cs.CL2020

European Language Grid: An Overview

Georg Rehm, Maria Berger, Ela Elsholz +33

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). Europea…

cs.CL2019

Speaker Adaptive Training using Model Agnostic Meta-Learning

Ondřej Klejch, Joachim Fainberg, Peter Bell +1

Speaker adaptive training (SAT) of neural network acoustic models learns models in a way that makes them more suitable for adaptation to test conditions. Conventionally, model-base…

eess.AS2019

Acoustic Model Adaptation from Raw Waveforms with SincNet

Joachim Fainberg, Ondřej Klejch, Erfan Loweimi +2

Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a…

cs.CL20196 cited

Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models

Ondrej Klejch, Joachim Fainberg, Peter Bell +1

Acoustic model adaptation to unseen test recordings aims to reduce the mismatch between training and testing conditions. Most adaptation schemes for neural network models require t…

cs.CL2019

Lattice-based lightly-supervised acoustic model training

Joachim Fainberg, Ondřej Klejch, Steve Renals +1

In the broadcast domain there is an abundance of related text data and partial transcriptions, such as closed captions and subtitles. This text data can be used for lightly supervi…