8 citations · 15 across the 17 of their papers we have counts for
6 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…
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks
Aneesh Muppidi, Martin Radfar
Although speech recognition has become a widespread technology, inferring emotion from speech signals still remains a challenge. To address this problem, this paper proposes a quat…
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…
The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input
Bradley He, Martin Radfar
In order to evaluate the performance of the attention based neural ASR under noisy conditions, the current trend is to present hours of various noisy speech data to the model and m…
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…