Liang Lu

Segmental Recurrent Neural Networks for End-to-end Speech Recognition

2016 · arXiv (Cornell University)

We study the segmental recurrent neural network for end-to-end acoustic modelling. This model connects the segmental conditional random field (CRF) with a recurrent neural network (RNN) used for feature extraction. Compared to most previous CRF-based …

Small-Footprint Highway Deep Neural Networks for Speech Recognition

2017 · IEEE/ACM Transactions on Audio Speech and Language Processing

State-of-the-art speech recognition systems typically employ neural network acoustic models. However, compared to Gaussian mixture models, deep neural network (DNN) based acoustic models often have many more model parameters, making it challenging for them to …

On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer

2021

Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end acoustic model that extends the standard Recurrent Neural Network Transducer (RNN-T) for the purpose of the external language model (LM) fusion.In HAT, the blank probability and …

A study of the recurrent neural network encoder-decoder for large vocabulary speech recognition

2015

Deep neural networks have advanced the state-of-the-art in automatic speech recognition, when combined with hidden Markov models (HMMs). Recently there has been interest in using systems based on recurrent neural networks (RNNs) to perform sequence …