9 citations · 32 across the 22 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Exploiting Large-scale Teacher-Student Training for On-device Acoustic Models
Jing Liu, Rupak Vignesh Swaminathan, Sree Hari Krishnan Parthasarathi +3
We present results from Alexa speech teams on semi-supervised learning (SSL) of acoustic models (AM) with experiments spanning over 3000 hours of GPU time, making our study one of…