1 citations · 2 across the 5 of their papers we have counts for
9 papers · 1 filter
Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction
Christophe Van Gysel, Maggie Wu, Lyan Verwimp +4
End-to-end (E2E) Automatic Speech Recognition (ASR) models are trained using paired audio-text samples that are expensive to obtain, since high-quality ground-truth data requires h…
Towards a World-English Language Model for On-Device Virtual Assistants
Rricha Jalota, Lyan Verwimp, Markus Nussbaum-Thom +3
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 m…
Application-Agnostic Language Modeling for On-Device ASR
Markus Nußbaum-Thom, Lyan Verwimp, Youssef Oualil
On-device automatic speech recognition systems face several challenges compared to server-based systems. They have to meet stricter constraints in terms of speed, disk size and mem…
On the long-term learning ability of LSTM LMs
Wim Boes, Robbe Van Rompaey, Lyan Verwimp +3
We inspect the long-term learning ability of Long Short-Term Memory language models (LSTM LMs) by evaluating a contextual extension based on the Continuous Bag-of-Words (CBOW) mode…
Error-driven Pruning of Language Models for Virtual Assistants
Sashank Gondala, Lyan Verwimp, Ernest Pusateri +2
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 can…
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?
Lyan Verwimp, Jerome R. Bellegarda
Natural language processing (NLP) tasks tend to suffer from a paucity of suitably annotated training data, hence the recent success of transfer learning across a wide variety of th…