18 citations · 38 across the 7 of their papers we have counts for
13 papers
Contextualization of ASR with LLM using phonetic retrieval-based augmentation
Zhihong Lei, Xingyu Na, Mingbin Xu +5
Large language models (LLMs) have shown superb capability of modeling multimodal signals including audio and text, allowing the model to generate spoken or textual response given a…
A Discriminative Entity-Aware Language Model for Virtual Assistants
Mandana Saebi, Ernest Pusateri, Aaksha Meghawat +1
High-quality automatic speech recognition (ASR) is essential for virtual assistants (VAs) to work well. However, ASR often performs poorly on VA requests containing named entities.…
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…
Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants
Christophe Van Gysel, Manos Tsagkias, Ernest Pusateri +1
We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions…
Connecting and Comparing Language Model Interpolation Techniques
Ernest Pusateri, Christophe Van Gysel, Rami Botros +4
In this work, we uncover a theoretical connection between two language model interpolation techniques, count merging and Bayesian interpolation. We compare these techniques as well…
Pytrec_eval: An Extremely Fast Python Interface to trec_eval
Christophe Van Gysel, Maarten de Rijke
We introduce pytrec_eval, a Python interface to the tree_eval information retrieval evaluation toolkit. pytrec_eval exposes the reference implementations of trec_eval within Python…