6 accepted papers
Nowadays, most methods for end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on phrase-level contextual modeling and attention-based relevance modeling, they may suffer from the confusion between simil…
In the recent trend of semi-supervised speech recognition, both self-supervised representation learning and pseudo-labeling have shown promising results. In this paper, we propose a novel approach to combine their ideas for end-to-end speech recognition model. Without any extra loss function, we uti…
The recurrent neural network transducer (RNN-T) model has been proved effective for keyword spotting (KWS) recently. However, compared with cross-entropy (CE) or connectionist temporal classification (CTC) based models, the additional prediction network in the RNN-T model increases the model size an…
Recently, a hybrid deep neural network/i-vector framework has been proved effective for speaker verification, where the DNN trained to predict tied-triphone states (senones) is used to produce frame alignments for sufficient statistics extraction. In this work, in order to better understand the impa…
Exploiting sparseness in deep neural networks is an important method for reducing the computational cost. In this paper, we study neuron sparseness in deep neural networks for acoustic modeling. For the feed-forward stage, we only activate neurons whose input values are larger than a given threshold…
The OpenKWS14 keyword search evaluation is one of the most challenging and influential evaluations in the field of speech recognition. Its goal is to build a high-performance keyword search system for a minority language with limited training data in a short period of time. We present the system of…