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Lyan Verwimp

4 accepted papers

2024

Towards A World-English Language Model for on-Device Virtual Assistants

Rricha Jalota, Lyan Verwimp, Markus Nußbaum-Thom, Amr El-Desoky Mousa, Arturo Argueta, Youssef Oualil
ICASSP 2024accepted

Neural Network Language Models (NNLMs) for Virtual Assistants (VAs) are generally language-, region-, and in some cases, device-dependent, which increases the effort to scale and maintain them. Combining NNLMs for one or more of the categories is one way to improve scalability. In this work, we comb…

Cited by 0Source ↗Scholar ↗
2021

Error-Driven Pruning of Language Models for Virtual Assistants

Sashank Gondala, Lyan Verwimp, Ernest Pusateri, Manos Tsagkias, Christophe Van Gysel
ICASSP 2021accepted

Language models (LMs) for virtual assistants (VAs) are typically trained on large amounts of data, resulting in prohibitively large models which require excessive memory and/or cannot be used to serve user requests in real-time. Entropy pruning results in smaller models but with significant degradat…

Cited by 0Source ↗Scholar ↗
2016

Language model adaptation for ASR of spoken translations using phrase-based translation models and named entity models

Joris Pelemans, Tom Vanallemeersch, Kris Demuynck, Lyan Verwimp, Hugo Van hamme, Patrick Wambacq
ICASSP 2016accepted

Language model adaptation based on Machine Translation (MT) is a recently proposed approach to improve the Automatic Speech Recognition (ASR) of spoken translations that does not suffer from a common problem in approaches based on rescoring i.e. errors made during recognition cannot be recovered by…

Cited by 4Source ↗Scholar ↗