9 citations · 20 across the 13 of their papers we have counts for
8 papers · 1 filter
Contextual Adapters for Personalized Speech Recognition in Neural Transducers
Kanthashree Mysore Sathyendra, Thejaswi Muniyappa, Feng-Ju Chang +5
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is…
A neural prosody encoder for end-ro-end dialogue act classification
Kai Wei, Dillon Knox, Martin Radfar +6
Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful…
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…
FANS: Fusing ASR and NLU for on-device SLU
Martin Radfar, Athanasios Mouchtaris, Siegfried Kunzmann +1
Spoken language understanding (SLU) systems translate voice input commands to semantics which are encoded as an intent and pairs of slot tags and values. Most current SLU systems d…
End-to-End Spoken Language Understanding for Generalized Voice Assistants
Michael Saxon, Samridhi Choudhary, Joseph P. McKenna +1
End-to-end (E2E) spoken language understanding (SLU) systems predict utterance semantics directly from speech using a single model. Previous work in this area has focused on target…
CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition
Rupak Vignesh Swaminathan, Brian King, Grant P. Strimel +2
We propose a simple yet effective method to compress an RNN-Transducer (RNN-T) through the well-known knowledge distillation paradigm. We show that the transducer's encoder outputs…