Conventional end-to-end Automatic Speech Recognition (ASR) models primar...
Improving the performance of end-to-end ASR models on long utterances ra...
The recurrent neural network transducer (RNN-T) has recently become the
...
End-to-end models have achieved state-of-the-art results on several auto...
Although end-to-end automatic speech recognition (e2e ASR) models are wi...
Streaming end-to-end automatic speech recognition (ASR) models are widel...
Uncertainty quantification is an important research area in machine lear...
In this paper, we propose to use pre-trained features from end-to-end AS...
We study a budgeted hyper-parameter tuning problem, where we optimize th...
We study large-scale kernel methods for acoustic modeling in speech
reco...
Recurrent neural networks (RNNs), including long short-term memory (LSTM...
We study large-scale kernel methods for acoustic modeling and compare to...
The computational complexity of kernel methods has often been a major ba...