  The Mandarin Chinese language is known to be strongly influenced by a rich
set of regional accents, while Mandarin speech with each accent is quite low
resource. Hence, an important task in Mandarin speech recognition is to
appropriately model the acoustic variabilities imposed by accents. In this
paper, an investigation of implicit and explicit use of accent information on a
range of deep neural network (DNN) based acoustic modelling techniques is
conducted. Meanwhile, approaches of multi-accent modelling including
multi-style training, multi-accent decision tree state tying, DNN tandem and
multi-level adaptive network (MLAN) tandem hidden Markov model (HMM) modelling
are combined and compared in this paper. On a low resource accented Mandarin
speech recognition task consisting of four regional accents, an improved MLAN
tandem HMM systems explicitly leveraging the accent information was proposed
and significantly outperformed the baseline accent independent DNN tandem
systems by 0.8%-1.5% absolute (6%-9% relative) in character error rate after
sequence level discriminative training and adaptation.
