16 citations · 26 across the 14 of their papers we have counts for
5 papers · 1 filter
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Kai Wei, Thanh Tran, Feng-Ju Chang +8
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio. While dialo…
Context-Aware Transformer Transducer for Speech Recognition
Feng-Ju Chang, Jing Liu, Martin Radfar +4
End-to-end (E2E) automatic speech recognition (ASR) systems often have difficulty recognizing uncommon words, that appear infrequently in the training data. One promising method, t…
Multi-Channel Transformer Transducer for Speech Recognition
Feng-Ju Chang, Martin Radfar, Athanasios Mouchtaris +1
Multi-channel inputs offer several advantages over single-channel, to improve the robustness of on-device speech recognition systems. Recent work on multi-channel transformer, has…
Sparsification via Compressed Sensing for Automatic Speech Recognition
Kai Zhen, Hieu Duy Nguyen, Feng-Ju Chang +3
In order to achieve high accuracy for machine learning (ML) applications, it is essential to employ models with a large number of parameters. Certain applications, such as Automati…
End-to-End Multi-Channel Transformer for Speech Recognition
Feng-Ju Chang, Martin Radfar, Athanasios Mouchtaris +2
Transformers are powerful neural architectures that allow integrating different modalities using attention mechanisms. In this paper, we leverage the neural transformer architectur…