Steve Renals

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 …

WERD: Using social text spelling variants for evaluating dialectal speech recognition

2017 · 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

We study the problem of evaluating automatic speech recognition (ASR) systems that target dialectal speech input. A major challenge in this case is that the orthography of dialects is typically not standardized. From an ASR …

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 …