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20182024
most citedLattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models

6 citations · 7 across the 7 of their papers we have counts for

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9 papers · 1 filter

cs.CL2023

Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling

Ramon Sanabria, Ondrej Klejch, Hao Tang +1

Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units. For unsupervised systems, these are mined using k-nearest neighbor (KN…

cs.CL2023★ 1 cited

The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR

Ramon Sanabria, Nikolay Bogoychev, Nina Markl +3

English is the most widely spoken language in the world, used daily by millions of people as a first or second language in many different contexts. As a result, there are many vari…

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.CL2021

Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR

Ondrej Klejch, Electra Wallington, Peter Bell

We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language, and with no phonetic knowledge of the language…

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…