9 citations · 27 across the 9 of their papers we have counts for
4 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…
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…
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…