Context dependent phone models for LSTM RNN acoustic modelling

Abstract

Long Short Term Memory Recurrent Neural Networks (LSTM RNNs), combined with hidden Markov models (HMMs), have recently been show to outperform other acoustic models such as Gaussian mixture models (GMMs) and deep neural networks (DNNs) for large scale speech recognition. We argue that using multi-state HMMs with LSTM RNN acoustic models is an unnecessary vestige of GMM-HMM and DNN-HMM modelling since LSTM RNNs are able to predict output distributions through continuous, instead of piece-wise stationary, modelling of the acoustic trajectory. We demonstrate equivalent results for context independent whole-phone or 3-state models and show that minimum-duration modelling can lead to improved results. We go on to show that context dependent whole-phone models can perform as well as context dependent states, given a minimum duration model.