Asynchronous, online, GMM-free training of a context dependent acoustic model for speech recognition
Michiel Bacchiani, Andrew Senior, Georg Heigold · 2014
We propose an algorithm that allows online training of a con-text dependent DNN model. It designs a state inventory based on DNN features and jointly optimizes the DNN parameters and alignment of the training data. The process allows flat starting a model from scratch and avoids any dependency on a GMM/HMM model to bootstrap the training process. A 15k state model trained with the proposed algorithm reduced the er-ror rate on a mobile speech task with 24 % compared to a system bootstrapped from a CI HMM/GMM and with 16 % compared to a system bootstrapped from a CD HMM/GMM system. Index Terms: Deep Neural Networks, online training 1.