Search Results for author:
Found 6 papers, 0 papers with code
Alternating Weak Triphone/BPE Alignment Supervision from Hybrid Model Improves End-to-End ASR
In this paper, alternating weak triphone/BPE alignment supervision is proposed to improve end-to-end model training.
Weak Alignment Supervision from Hybrid Model Improves End-to-end ASR
In contrast to the general one-hot cross-entropy losses, here we use a cross-entropy loss with a label smoothing parameter to regularize the supervision.
Improving End-to-End Models for Set Prediction in Spoken Language Understanding
The goal of spoken language understanding (SLU) systems is to determine the meaning of the input speech signal, unlike speech recognition which aims to produce verbatim transcripts.
Reducing Exposure Bias in Training Recurrent Neural Network Transducers
By reducing the exposure bias, we show that we can further improve the accuracy of a high-performance RNNT ASR model and obtain state-of-the-art results on the 300-hour Switchboard dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-end spoken language understanding using transformer networks and self-supervised pre-trained features
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 the field of spoken language understanding (SLU) still need further investigation.
English Broadcast News Speech Recognition by Humans and Machines
With recent advances in deep learning, considerable attention has been given to achieving automatic speech recognition performance close to human performance on tasks like conversational telephone speech (CTS) recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
