  Evolutionary stochastic gradient descent (ESGD) was proposed as a
population-based approach that combines the merits of gradient-aware and
gradient-free optimization algorithms for superior overall optimization
performance. In this paper we investigate a variant of ESGD for optimization of
acoustic models for automatic speech recognition (ASR). In this variant, we
assume the existence of a well-trained acoustic model and use it as an anchor
in the parent population whose good "gene" will propagate in the evolution to
the offsprings. We propose an ESGD algorithm leveraging the anchor models such
that it guarantees the best fitness of the population will never degrade from
the anchor model. Experiments on 50-hour Broadcast News (BN50) and 300-hour
Switchboard (SWB300) show that the ESGD with anchors can further improve the
loss and ASR performance over the existing well-trained acoustic models.

Computation and Language;Machine Learning;Audio and Speech Processing