10 citations · 18 across the 8 of their papers we have counts for
8 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…
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…
A Neural Network Approach for Mixing Language Models
Youssef Oualil, Dietrich Klakow
The performance of Neural Network (NN)-based language models is steadily improving due to the emergence of new architectures, which are able to learn different natural language cha…
Long-Short Range Context Neural Networks for Language Modeling
Youssef Oualil, Mittul Singh, Clayton Greenberg +1
The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learni…
A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models
Youssef Oualil, Dietrich Klakow
Training large vocabulary Neural Network Language Models (NNLMs) is a difficult task due to the explicit requirement of the output layer normalization, which typically involves the…
Sequential Recurrent Neural Networks for Language Modeling
Youssef Oualil, Clayton Greenberg, Mittul Singh +1
Feedforward Neural Network (FNN)-based language models estimate the probability of the next word based on the history of the last N words, whereas Recurrent Neural Networks (RNN) p…