7 citations · 8 across the 8 of their papers we have counts for
10 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…
Retrieval Augmented Correction of Named Entity Speech Recognition Errors
Ernest Pusateri, Anmol Walia, Anirudh Kashi +6
In recent years, end-to-end automatic speech recognition (ASR) systems have proven themselves remarkably accurate and performant, but these systems still have a significant error r…
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization
Zhihong Lei, Ernest Pusateri, Shiyi Han +8
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as c…
Acoustic Model Fusion for End-to-end Speech Recognition
Zhihong Lei, Mingbin Xu, Shiyi Han +8
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level. The E2E systems implic…
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…
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 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…