12 citations · 20 across the 5 of their papers we have counts for
7 papers
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing
Xiaodong Cui, George Saon, Tohru Nagano +4
We introduce two techniques, length perturbation and n-best based label smoothing, to improve generalization of deep neural network (DNN) acoustic models for automatic speech recog…
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 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…
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation
Gakuto Kurata, Kartik Audhkhasi
Conventional automatic speech recognition (ASR) systems trained from frame-level alignments can easily leverage posterior fusion to improve ASR accuracy and build a better single m…
Language Modeling with Highway LSTM
Gakuto Kurata, Bhuvana Ramabhadran, George Saon +1
Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks. In this paper, we extend an LSTM by adding highway ne…