12 citations · 16 across the 10 of their papers we have counts for
5 papers · 1 filter
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
Vishal Sunder, Samuel Thomas, Hong-Kwang J. Kuo +3
Dialog history plays an important role in spoken language understanding (SLU) performance in a dialog system. For end-to-end (E2E) SLU, previous work has used dialog history in tex…
Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems
Samuel Thomas, Hong-Kwang J. Kuo, Brian Kingsbury +1
The lack of speech data annotated with labels required for spoken language understanding (SLU) is often a major hurdle in building end-to-end (E2E) systems that can directly proces…
Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models
Samuel Thomas, Brian Kingsbury, George Saon +1
Compared to hybrid automatic speech recognition (ASR) systems that use a modular architecture in which each component can be independently adapted to a new domain, recent end-to-en…
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
Zvi Kons, Aharon Satt, Hong-Kwang Kuo +4
Intent classifiers are vital to the successful operation of virtual agent systems. This is especially so in voice activated systems where the data can be noisy with many ambiguous…
Improving End-to-End Models for Set Prediction in Spoken Language Understanding
Hong-Kwang J. Kuo, Zoltan Tuske, Samuel Thomas +2
The goal of spoken language understanding (SLU) systems is to determine the meaning of the input speech signal, unlike speech recognition which aims to produce verbatim transcripts…