11 citations · 11 across the 4 of their papers we have counts for
5 papers
Multi-View Frequency-Attention Alternative to CNN Frontends for Automatic Speech Recognition
Belen Alastruey, Lukas Drude, Jahn Heymann +1
Convolutional frontends are a typical choice for Transformer-based automatic speech recognition to preprocess the spectrogram, reduce its sequence length, and combine local informa…
Contextual-Utterance Training for Automatic Speech Recognition
Alejandro Gomez-Alanis, Lukas Drude, Andreas Schwarz +2
Recent studies of streaming automatic speech recognition (ASR) recurrent neural network transducer (RNN-T)-based systems have fed the encoder with past contextual information in or…
Improving RNN-T ASR Accuracy Using Context Audio
Andreas Schwarz, Ilya Sklyar, Simon Wiesler
We present a training scheme for streaming automatic speech recognition (ASR) based on recurrent neural network transducers (RNN-T) which allows the encoder network to learn to exp…
Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition
Egor Lakomkin, Jahn Heymann, Ilya Sklyar +1
Subwords are the most widely used output units in end-to-end speech recognition. They combine the best of two worlds by modeling the majority of frequent words directly and at the…
Compacting Neural Network Classifiers via Dropout Training
Yotaro Kubo, George Tucker, Simon Wiesler
We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization…