12 citations · 16 across the 2 of their papers we have counts for
9 papers
RNN Transducer Models For Spoken Language Understanding
Samuel Thomas, Hong-Kwang J. Kuo, George Saon +5
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU). These end-to-end (E2E) models are constructed in thr…
End-to-end spoken language understanding using transformer networks and self-supervised pre-trained features
Edmilson Morais, Hong-Kwang J. Kuo, Samuel Thomas +2
Transformer networks and self-supervised pre-training have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in t…
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Yinghui Huang, Hong-Kwang Kuo, Samuel Thomas +5
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is…
End-to-End Spoken Language Understanding Without Full Transcripts
Hong-Kwang J. Kuo, Zoltán Tüske, Samuel Thomas +7
An essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels. In this paper, we develo…
English Broadcast News Speech Recognition by Humans and Machines
Samuel Thomas, Masayuki Suzuki, Yinghui Huang +8
With recent advances in deep learning, considerable attention has been given to achieving automatic speech recognition performance close to human performance on tasks like conversa…
Understanding Unequal Gender Classification Accuracy from Face Images
Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha +7
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Acc…