LEARNING EFFECTIVE FACTORIZED HIDDEN LAYER BASES USING STUDENT-TEACHER TRAINING FOR LSTM ACOUSTIC MODEL ADAPTATION

Factorized Hidden Layer (FHL) has been proposed for the adaptation of deep neural network (DNN) and Long Short-Term Memory (LSTM) based acoustic models (AMs). In FHL, a speaker-dependent (SD) transformation matrix and an SD bias are included in addition to the standard affine transformation. The SD transformation is a linear combination of rank-1 matrices whereas the SD bias is a linear combination of vectors. However, the adaptation of LSTMs is challenging and often reports modest gains. In this paper, we propose to use student-teacher training to estimate more efficient FHL bases for LSTM AMs using an FHL adapted DNN as the teacher model. For both AMI IHM and AMI SDM tasks, FHL achieves 3.2% absolute improvement over the frame-level cross entropy trained LSTM base-lines. Moreover, FHL results 3.0% and 3.8% absolute improvements over sequentially trained LSTM baselines for the AMI IHM and AMI SDM tasks respectively.