  Conventional automatic speech recognition (ASR) systems trained from
frame-level alignments can easily leverage posterior fusion to improve ASR
accuracy and build a better single model with knowledge distillation.
End-to-end ASR systems trained using the Connectionist Temporal Classification
(CTC) loss do not require frame-level alignment and hence simplify model
training. However, sparse and arbitrary posterior spike timings from CTC models
pose a new set of challenges in posterior fusion from multiple models and
knowledge distillation between CTC models. We propose a method to train a CTC
model so that its spike timings are guided to align with those of a pre-trained
guiding CTC model. As a result, all models that share the same guiding model
have aligned spike timings. We show the advantage of our method in various
scenarios including posterior fusion of CTC models and knowledge distillation
between CTC models with different architectures. With the 300-hour Switchboard
training data, the single word CTC model distilled from multiple models
improved the word error rates to 13.7%/23.1% from 14.9%/24.1% on the Hub5 2000
Switchboard/CallHome test sets without using any data augmentation, language
model, or complex decoder.
