2 citations · 2 across the 12 of their papers we have counts for
7 papers · 1 filter
Extending RNN-T-based speech recognition systems with emotion and language classification
Zvi Kons, Hagai Aronowitz, Edmilson Morais +4
Speech transcription, emotion recognition, and language identification are usually considered to be three different tasks. Each one requires a different model with a different arch…
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…
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems
Vishal Sunder, Eric Fosler-Lussier, Samuel Thomas +2
Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have been primarily due to effective pretraining of speech representations. One such pretraining paradigm is…
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…