research
∙
04/11/2022
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems
Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) ...
share
research
∙
04/11/2022
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
Dialog history plays an important role in spoken language understanding ...
research
∙
02/26/2022
Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems
The lack of speech data annotated with labels required for spoken langua...
research
∙
02/26/2022
Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models
Compared to hybrid automatic speech recognition (ASR) systems that use a...
research
∙
01/28/2022
Improving End-to-End Models for Set Prediction in Spoken Language Understanding
The goal of spoken language understanding (SLU) systems is to determine ...
research
∙
08/18/2021
Integrating Dialog History into End-to-End Spoken Language Understanding Systems
End-to-end spoken language understanding (SLU) systems that process huma...
research
∙
04/08/2021
RNN Transducer Models For Spoken Language Understanding
We present a comprehensive study on building and adapting RNN transducer...
research
∙
11/16/2020
End-to-end spoken language understanding using transformer networks and self-supervised pre-trained features
Transformer networks and self-supervised pre-training have consistently ...
research
∙
09/30/2020
End-to-End Spoken Language Understanding Without Full Transcripts
An essential component of spoken language understanding (SLU) is slot fi...
research
∙
04/27/2016
