10 citations · 18 across the 8 of their papers we have counts for
11 papers
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…
Synthetic Query Generation using Large Language Models for Virtual Assistants
Sonal Sannigrahi, Thiago Fraga-Silva, Youssef Oualil +1
Virtual Assistants (VAs) are important Information Retrieval platforms that help users accomplish various tasks through spoken commands. The speech recognition system (speech-to-te…
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…
Neural Language Model Pruning for Automatic Speech Recognition
Leonardo Emili, Thiago Fraga-Silva, Ernest Pusateri +2
We study model pruning methods applied to Transformer-based neural network language models for automatic speech recognition. We explore three aspects of the pruning frame work, nam…
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…
Space-Efficient Representation of Entity-centric Query Language Models
Christophe Van Gysel, Mirko Hannemann, Ernest Pusateri +2
Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries. However, spoken entity recognition is a difficult problem, due to the…